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Detection of glaucoma using retinal fundus images: A comprehensive review

  • Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.

    Citation: Amsa Shabbir, Aqsa Rasheed, Huma Shehraz, Aliya Saleem, Bushra Zafar, Muhammad Sajid, Nouman Ali, Saadat Hanif Dar, Tehmina Shehryar. Detection of glaucoma using retinal fundus images: A comprehensive review[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2033-2076. doi: 10.3934/mbe.2021106

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  • Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.



    Lagrange's theorem is one of the most fundamental theorem of Abstract algebra. In the late 18th century, Lagrange's theorem first appeared to handle the polynomial equation of degree five or more and its relation with symmetric functions. But, Lagrange stated his version of the theorem before the invention of group theory. This theorem developed over the decades. Pietro first gave the complete proof of this theorem. This theorem is an important tool for the study of finite groups as it gives an overview of the size of subgroups. Lagrange's theorem has various applications in number theory. This theorem has a significant role in the proof of Fermat's Little theorem. For further details, we refer to [1,2].

    Uncertainty is a part of our daily life. This world is neither based on hypothesis nor on accurate measurements. There is not always possible to make an obvious decision. Handling the errors in decision-making situation is a real challenge for us. In 1965, Zadeh [3] first introduced the notion of fuzzy set to handle vagueness in real-life problems, where he broke the conventional idea of yes or not that is zero or one. He defined fuzzy set as a mapping from any set to [0,1]. So the membership value of an element is any number between 0 and 1. After that fuzzy set becomes a trend in worldwide research. In 1971, Rosenfeld [4] first studied the concept of fuzzy subgroup and investigated various properties of it. In 1979, the notion of fuzzy subgroup was redefined by Anthony and Sherwood [5,6]. Fuzzy level subgroups were introduced by Das [7]. In 1992, Ajmal and Prajapati [8] introduced the ideas of fuzzy normal subgroup, fuzzy coset and fuzzy quotient subgroup. In 1988, Choudhury et al. [9] proved various properties of fuzzy subgroups and fuzzy homomorphisms. In 1990, Dixit et al. [10] discussed fuzzy level subgroups and union of fuzzy subgroups. The notion of anti-fuzzy subgroup was first proposed by Biswas [11]. Chakraborty and Khare [12] studied various properties of fuzzy homomorphisms. Ajmal [13] also studied homomorphisms of fuzzy subgroups. In 1994, Kim [14] defined the order of fuzzy subgroups and fuzzy p-subgroups. Many more results on fuzzy subgroups were introduced by Mukherjee [15,16] and Bhattacharya [17,18]. In 1999, Ray [19] introduced the product of fuzzy subgroups. In recent years many researchers studied various properties of fuzzy groups. In 2015, Tarnauceanu [20] classified fuzzy normal subgroups of finite groups. In 2016, Onasanya [21] reviewed some anti fuzzy properties of fuzzy subgroups. Shuaib [22] and Shaheryar [23] studied the properties of omicron fuzzy subgroups and omicron anti fuzzy subgroups. In 2018, Addis [24] developed fuzzy homomorphism theorems on groups.

    In decision-making problems, sometimes assigning membership values is not enough. In 1986, assigning non-membership degree with membership degree Atanassov [25] developed intuitionistic fuzzy set. Using this concept, intuitionistic fuzzy subgroups were studied by Zhan and Tan [26]. In 2013, Yager [27] defined Pythagorean fuzzy set. This set gives a modern way to model vagueness and uncertainty with high precision and accuracy compared to intuitionistic fuzzy sets. In 2021, Bhunia et al. [28] introduced Pythagorean fuzzy subgroups. In recent years, some results related to Pythagorean fuzzy sets were given by [29,30,31,32]. In 2021, Bhunia [33] and Ghorai first initiated the study of (α,β)-Pythagorean fuzzy sets, where they imposed the restrictions α and β for more accuracy. They proved that (α,β)-Pythagorean fuzzy sets are more precise than intuitionistic fuzzy sets and Pythagorean fuzzy sets. They defined the notion of (α,β)-Pythagorean fuzzy subgroup and proved various algebraic aspects of it. One of the most important results of finite group theory is the Lagrange's theorem. Our main motive of this paper is to give an (α,β)-Pythagorean fuzzy version of Lagrange's theorem in (α,β)-Pythagorean fuzzy subgroups. We introduce the concept of relative subgroup of a group and define the (α,β)-Pythagorean fuzzy order of an element in (α,β)-Pythagorean fuzzy subgroups. We make a comparison between order of an element in a group and (α,β)-Pythagorean fuzzy order of that element in (α,β)-Pythagorean fuzzy subgroup. We define the extension principle in (α,β)-Pythagorean fuzzy environments and study the effect of group homomorphism on (α,β)-Pythagorean fuzzy subgroups. We introduce (α,β)-Pythagorean fuzzy quotient group and the index of an (α,β)-Pythagorean fuzzy subgroup. Finally, we give an approach to Lagrange's theorem in (α,β)-Pythagorean fuzzy subgroups.

    An outline of this paper is given as follows: we recall some important definitions and concepts in Section 2. In Section 3, we define the notion of (α,β)-Pythagorean fuzzy order of elements of groups and discuss many properties of it. Section 4 deals with some algebraic attributes of (α,β)-Pythagorean fuzzy subgroup. In Section 5, we introduce the concept of (α,β)-Pythagorean fuzzy quotient group and give an (α,β)-Pythagorean fuzzy version of Lagrange's theorem. Finally, we make a conclusion in Section 6.

    In this section, we recall some basic definitions which are used for the development of later sections.

    Definition 2.1. [33] Let S be a crisp set and α, β[0,1] be such that 0α2+β21. An (α,β)-Pythagorean fuzzy set ψ in S is an object having the form ψ={(u,μα(u),νβ(u)|uS} where μα(u)=μ(u)α and νβ(u)=ν(u)β are membership degree and non-membership degree of uS respectively, which satisfies the condition 0(μα(u))2+(νβ(u))21.

    Definition 2.2. [33] Let ψ be an (α,β)-Pythagorean fuzzy set of a group G. Then ψ is said to be an (α,β)-Pythagorean fuzzy subgroup of the group G if the following conditions hold:

    μα(uv)μα(u)μα(v) and νβ(uv)νβ(u)νβ(v) for all u,vG

    μα(u1)μα(u) and νβ(u1)νβ(u) for all uG.

    Throughout this paper, we will write (α,β)-Pythagorean fuzzy set as (α,β)-PFS and (α,β)-Pythagorean fuzzy subgroup as (α,β)-PFSG. Also, we will denote (α,β)-PFS as ψ=(μα,νβ) instead of ψ={(a,μα(a),νβ(a)|aS}.

    Proposition 2.1. [33] Let ψ=(μα,νβ) be an (α,β)-PFS of a group G. Then ψ is an (α,β)-PFSG of G if and only if μα(uv1)μα(u)μα(v) and νβ(uv1)νβ(u)νβ(v) for all u,vG.

    Definition 2.3. [33] Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then for uG, (α,β)-Pythagorean fuzzy left coset (PFLC) of ψ is the (α,β)-PFS uψ=(uμα,uνβ), defined by (uμα)(m)=μα(u1m), (uνβ)(m)=νβ(u1m) and (α,β)-Pythagorean fuzzy right coset (PFRC) of ψ is the (α,β)-PFS ψu=(μαu,νβu), defined by (μαu)(m)=μα(mu1), (νβu)(m)=νβ(mu1) for all mG.

    Definition 2.4. [33] Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then ψ is an (α,β)-Pythagorean fuzzy normal subgroup (PFNSG) of the group G if every (α,β)-PFLC of ψ is also an (α,β)-PFRC of ψ in G.

    Equivalently, uψ=ψu for all uG.

    Proposition 2.2. [33] Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then ψ is an (α,β)-PFNSG of G if and only if μα(uv)=μα(vu) and νβ(uv)=νβ(vu) for all u,vG.

    Proposition 2.3. [33] Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then ψ is an (α,β)-PFNSG of G if and only if μα(vuv1)=μα(u) and νβ(vuv1)=νβ(u) for all u,vG.

    In this section, we introduce the concept of relative subgroup of a group and define the (α,β)-Pythagorean fuzzy order of elements in (α,β)-Pythagorean fuzzy subgroups. Further, We compare between the fuzzy order of elements in fuzzy subgroups and (α,β)-Pythagorean fuzzy order of elements in (α,β)-Pythagorean fuzzy subgroups. Moreover, we discuss various algebraic properties of (α,β)-Pythagorean fuzzy order of elements in (α,β)-PFSGs.

    First of all, we will construct a theorem which will be the building block of this section.

    Theorem 3.1. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G and u be any element of G. Then Ω(u)={vG|μα(v)μα(u), νβ(v)νβ(u)} forms a subgroup of the group G.

    Proof. For any element uG, we have Ω(u)={vG|μα(v)μα(u), νβ(v)νβ(u)}.

    Clearly uΩ(u), so Ω(u) is a non empty subset of G.

    Since ψ=(μα,νβ) is an (α,β)-PFSG of the group G, then μα(e)μα(u) and νβ(e)νβ(u), where e is the identity element of G. So, eΩ(u).

    Let m and n be two arbitrary elements of Ω(u). Therefore

    μα(mn1)μα(m)μα(n1)=μα(m)μα(n)μα(u).

    Similarly, we can show that νβ(mn1)νβ(u).

    Consequently mn1Ω(u).

    Hence Ω(u) is a subgroup of the group G.

    Definition 3.1. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G and u be any element of G. Then the subgroup Ω(u) is called the relative subgroup of the group G corresponding to the element u.

    Definition 3.2. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G and u be any element of G. Then the (α,β)-Pythagorean fuzzy order of u in ψ is denoted by (α,β)-PFO (u)ψ and defined by the order of the relative subgroup of u in G.

    Therefore, (α,β)-PFO (u)ψ=O(Ω(u)) for all u in G.

    Example 3.1. Let us consider the Klein's 4-group V4={e,a,b,c}, where e is the identity element of V4 and each element has its own inverse. Define the membership degree and non-membership degree of the elements of V4 by

    μ(e)=0.9, μ(a)=0.6, μ(b)=0.6, μ(c)=0.8,ν(e)=0.3, ν(a)=0.8, ν(b)=0.8, ν(c)=0.5.

    We choose α=0.8 and β=0.5. Then an (α,β)-PFS ψ=(μα,νβ) of V4 is given by

    μα(e)=0.8, μα(a)=0.6, μα(b)=0.6, μα(c)=0.8,νβ(e)=0.5, νβ(a)=0.8, νβ(b)=0.8, νβ(c)=0.5.

    Clearly, ψ=(μα,νβ) is an (α,β)-PFSG of the group V4.

    Then the (α,β)-Pythagorean fuzzy order of the elements of V4 in ψ is given by

    (α,β)-PFO (e)ψ=O(Ω(e))=2, (α,β)-PFO (a)ψ=O(Ω(a))=4,

    (α,β)-PFO (b)ψ=O(Ω(b))=4 and (α,β)-PFO (c)ψ=O(Ω(c))=2.

    Now, we will make a comparison between order and (α,β)-Pythagorean fuzzy order of an element in a group.

    From the above example, we can see that (α,β)-PFO (e)ψO(e) and (α,β)-PFO (e)ψ=(α,β)-PFO (c)ψ=2. Again there is no element of order four in the Klein's 4-group but here (α,β)-Pythagorean fuzzy order of a and b in ψ is four.

    Remark 3.1. For any group, the identity element is the unique element of order one but in (α,β)-PFSG the (α,β)-Pythagorean fuzzy order of identity element may not be equal to one. Also, (α,β)-Pythagorean fuzzy order of an element in (α,β)-PFSG may not be equal to the order of that element in the group.

    Proposition 3.1. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then (α,β)-PFO (e)ψ(α,β)-PFO (u)ψ for all uG, where e is the identity of G.

    Proof. Let (α,β)-PFO (e)ψ=p, where p is a positive integer.

    Assume that Ω(e)={u1,u2,,up}, where uiuj for all i, j.

    Then μα(u1)=μα(u2)==μα(up)=μα(e) and

    νβ(u1)=νβ(u2)==νβ(up)=νβ(e).

    Since ψ=(μα,νβ) is an (α,β)-PFSG of the group G, then

    μα(e)μα(u) and νβ(e)νβ(u) for all uG.

    So, u1, u2, , upΩ(u). Thus Ω(e)Ω(u).

    Consequently, O(Ω(e))O(Ω(u)) for all uG.

    Hence (α,β)-PFO (e)ψ(α,β)-PFO (u)ψ for all uG.

    Theorem 3.2. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then for all uG, (α,β)-PFO (u)ψ=(α,β)-PFO (u1)ψ.

    Proof. Let u be an element of G.

    Then (α,β)-PFO (u)ψ=O(Ω(u)), where Ω(u)={vG|μα(v)μα(u), νβ(v)νβ(u)}.

    Since ψ=(μα,νβ) is an (α,β)-PFSG of the group G, therefore μα(u)=μα(u1) and νβ(u)=νβ(u1).

    So Ω(u)={vG|μα(v)μα(u1), νβ(v)νβ(u1)}=Ω(u1).

    This implies that, O(Ω(u))=O(Ω(u1)).

    Hence, (α,β)-PFO (u)ψ=(α,β)-PFO (u1)ψ.

    Since u is an arbitrary element of G,

    (α,β)-PFO (u)ψ=(α,β)-PFO (u1)ψ for all uG.

    Now, we will now introduce (α,β)-Pythagorean fuzzy order of (α,β)-PFSG of a group.

    Definition 3.3. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then (α,β)- Pythagorean fuzzy order of the (α,β)-PFSG ψ is denoted by (α,β)-PFO (ψ) and is defined by (α,β)-PFO (ψ)={(α,β)-PFO (u)ψ|uG}.

    Example 3.2. We consider the (α,β)-PFSG ψ of Klein's 4-group V4 in Example 3.1.

    (α,β)-Pythagorean fuzzy order of the elements of V4 in ψ is given by

    (α,β)-PFO (e)ψ=2, (α,β)-PFO (a)ψ=4, (α,β)-PFO (b)ψ=4 and (α,β)-PFO (c)ψ=2.

    Therefore (α,β)-PFO (ψ)={(α,β)-PFO (u)ψ|uV4}=4.

    Theorem 3.3. For any (α,β)-PFSG of a group, (α,β)-Pythagorean fuzzy order of that (α,β)-PFSG is same as the order of the group.

    Proof. Suppose ψ=(μα,νβ) be an (α,β)-PFSG of a group G.

    Let u be any element of G.

    Without loss of generality, we may assume that

    μα(v)μα(u) and νβ(v)νβ(u) for all vG.

    Since Ω(u)={vG|μα(v)μα(u), νβ(v)νβ(u)}, then Ω(u)=G.

    Also, |Ω(u)||Ω(v)| for all vG.

    Consequently (α,β)-PFO (ψ)=(α,β)-PFO (u)ψ.

    Again (α,β)-PFO (u)ψ=O(Ω(u)).

    Therefore (α,β)-PFO (ψ)=O(G).

    Hence (α,β)-Pythagorean fuzzy order of any (α,β)-PFSG of a group is the same as the order of the group.

    Remark 3.2. For any (α,β)-PFSG of a group G, (α,β)-Pythagorean fuzzy order of each element of G divides (α,β)-Pythagorean fuzzy order of the (α,β)-PFSG.

    Theorem 3.4. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G and u be an element of G such that (α,β)-PFO (u)ψ=p. If gcd(p,q)=1 for a positive integer q, then μα(uq)=μα(u) and νβ(uq)=νβ(u).

    Proof. Since (α,β)-PFO (u)ψ=p, then we have up=e.

    Again ψ=(μα,νβ) is an (α,β)-PFSG of a group G, then

    μα(uq)μα(u) and νβ(uq)νβ(u) for any positive integer q.

    Also gcd(p,q)=1, then there exists positive integers a and b such that ap+bq=1. Now

    μα(u)=μα(uap+bq)μα(uap)μα(ubq)μα(e)μα(uq)=μα(uq).

    Therefore μα(u)μα(uq). Similarly, we can show that νβ(u)νβ(uq).

    Hence μα(uq)=μα(u) and νβ(uq)=νβ(u).

    Theorem 3.5. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G and u be any element of G. If for an integer q, μα(uq)=μα(e) and νβ(uq)=νβ(e) then q|(α,β)-PFO (u)ψ.

    Proof. Let (α,β)-PFO (u)ψ=p.

    Without loss of generality, we may assume that q is the minimum integer for which μα(uq)=μα(e) and νβ(uq)=νβ(e) holds.

    By division algorithm, there exists two integers m and r such that p=mq+r where 0r<q. Now \newpage

    μα(ur)=μα(upmq)μα(up)μα((u1)mq)=μα(up)μα(umq)=μα(e)μα((uq)m)μα(e)μα(uq)=μα(e).

    Similarly, we can show that νβ(ur)νβ(e).

    Consequently, μα(ur)=μα(e) and νβ(ur)=νβ(e).

    This result contradicts the minimality of q as 0r<q.

    Therefore r must be zero, so p=mq.

    Hence q|(α,β)-PFO (u)ψ.

    In the next result, we will show how to find (α,β)-Pythagorean fuzzy order of integral power of an element.

    Theorem 3.6. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G and u be any element of G. If (α,β)-PFO (u)ψ=p then for an integer k, (α,β)-PFO (uk)ψ=pgcd(p,k).

    Proof. Let (α,β)-PFO (uk)ψ=m and assume that gcd(p,k)=g.

    Since (α,β)-PFO (u)ψ=p, then up=e where e is the identity of G. Now

    μα((uk)pg)=μα((up)kg)=μα(ekg)=μα(e).

    Similarly, we can show that νβ((uk)pg)=νβ(e).

    Therefore by Theorem 3.5, we can say pg divides m.

    Also we have gcd(p,k)=g, then there exists two integers s and t such that ps+kt=g. Therefore

    μα(ugm)=μα(u(ps+kt)m)=μα(upsmuktm)μα((up)sm)μα((ukm)t)μα(up)μα((uk)m)=μα(e)μα(e)=μα(e).

    Therefore the only possibility is μα(ugm)=μα(e).

    Similarly, we can prove that νβ(ugm)=νβ(e).

    Thus using Theorem 3.5 we have gm|p, that is m|pg.

    Therefore m=pg.

    Hence (α,β)-PFO (uk)ψ=pgcd(p,k).

    Theorem 3.7. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G and u be any element of G. If (α,β)-PFO (u)ψ=p and rs(mod p) then (α,β)-PFO (ur)ψ=(α,β)-PFO (us)ψ, where r,sZ.

    Proof. Let (α,β)-PFO (ur)ψ=x and (α,β)-PFO (us)ψ=y.

    Since rs(mod p) then r=pq+s for some integer q. Now

    μα((ur)y)=μα((upq+s)y)=μα(upqyusy)μα((up)qy)μα((us)y)=μα(e)μα(e)=μα(e).

    Therefore the only possibility is μα((ur)y)=μα(e).

    Similarly, we can prove νβ((ur)y)=νβ(e).

    By using Theorem 3.5, we have y|x.

    In the same manner, we can prove that x|y. Thus x=y.

    Hence (α,β)-PFO (ur)ψ=(α,β)-PFO (us)ψ, where r,sZ.

    Theorem 3.8. Let ψ=(μα,νβ) be an (α,β)-PFNSG of a group G and u be any element of G. Then (α,β)-PFO (u)ψ=(α,β)-PFO (vuv1)ψ for all vG.

    Proof. Let v be an arbitrary element of G.

    Since ψ is an (α,β)-PFNSG of the group G, then μα(u)=μα(vuv1) and νβ(u)=νβ(vuv1).

    Therefore the relative subgroups corresponding to u is the same as that of vuv1.

    This implies that Ω(u)=Ω(vuv1).

    Consequently, O(Ω(u))=O(Ω(vuv1)).

    Since v is an arbitrary element of G, hence (α,β)-PFO (u)ψ=(α,β)-PFO (vuv1)ψ for all vG.

    Theorem 3.9. Let ψ=(μα,νβ) be an (α,β)-PFNSG of a group G. Then (α,β)-PFO (uv)ψ=(α,β)-PFO (vu)ψ for all u,vG.

    Proof. Let u and v be any two elements of G.

    We have μα(uv)=μα((v1v)(uv))=μα(v1(vu)v).

    Similarly, νβ(uv)=νβ(v1(vu)v).

    Therefore Ω(uv)=Ω(v1(vu)(v1)1).

    Consequently, (α,β)-PFO (uv)ψ=(α,β)-PFO (v1(vu)(v1)1)ψ.

    Again by Theorem 3.8, we have (α,β)-PFO (v(vu)v1)ψ=(α,β)-PFO (vu)ψ.

    Since u and v are arbitrary elements of G, hence (α,β)-PFO (uv)ψ=(α,β)-PFO (vu)ψ for all u,vG.

    Theorem 3.10. Let ψ=(μα,νβ) be an (α,β)-PFSG of an abelian group G and u,v be two elements of G such that gcd((α,β)-PFO (u)ψ,(α,β)-PFO (v)ψ)=1. If μα(uv)=μα(e) and νβ(uv)=νβ(e) then (α,β)-PFO (u)ψ=(α,β)-PFO (v)ψ=1.

    Proof. Let (α,β)-PFO (u)ψ=p and (α,β)-PFO (v)ψ=q.

    So, we have gcd(p,q)=1. Now

    μα(uqvq)=μα((uv)q)μα(uv)=μα(e).

    Therefore the only possibility is μα(uqvq)=μα(e). Again

    μα(uq)=μα(uqvqvq)μα(uqvq)μα((v1)q)=μα(e)μα(e)=μα(e).

    So, we have μα(uq)=μα(e). Similarly, we can prove that νβ(uq)=νβ(e).

    Therefore by Theorem 3.5, we have q|p. Again gcd(p,q)=1, thus q=1.

    Similarly, we can show that p=1.

    Hence (α,β)-PFO (u)ψ=(α,β)-PFO (v)ψ=1.

    Theorem 3.11. Let ψ=(μα,νβ) be an (α,β)-PFSG of a cyclic group G. Then any two generators of the cyclic group G have same (α,β)-Pythagorean fuzzy order in ψ.

    Proof. Let G be a finite cyclic group of order n.

    Also, let u and v be two generators of G. Therefore un=e and vn=e.

    Since u is a generator of G, then v=uk for some positive integer k.

    So, k and n are co-prime that is gcd(k,n)=1.

    Therefore by applying Theorem 3.10, (α,β)-PFO (u)ψ=(α,β)-PFO (uk)ψ=(α,β)-PFO (v)ψ.

    Now, if G is an infinite cyclic group then it has only two generators.

    Suppose u is a generator of G then u1 is the only other generator.

    Therefore by using Theorem 3.2, we have (α,β)-PFO (u)ψ=(α,β)-PFO (u1)ψ.

    Hence (α,β)-Pythagorean fuzzy order of any two generators of a cyclic group is equal.

    In this section, we define the extension principle in (α,β)-Pythagorean fuzzy environment. We study the effect of group homomorphism on (α,β)-Pythagorean fuzzy subgroups. Further, we develop the concept of (α,β)-Pythagorean fuzzy normalizer and (α,β)-Pythagorean fuzzy centralizer. Moreover, we investigate many algebraic attributes of it.

    Definition 4.1. Let ψ1=(μα1,νβ1) and ψ2=(μα2,νβ2) be two (α,β)-Pythagorean fuzzy sets on G1 and G2 respectively. Let h be a mapping from G1 to G2. Then h(ψ1) is an (α,β)-Pythagorean fuzzy set on G2 and defined by h(ψ1)(v)=(h(μα1)(v),h(νβ1)(v)) for all vG2, where

    h(μα1)(v)={{μα1(u)|uG1 and h(u)=v},whenh1(v)0,elsewhere

    and

    h(νβ1)(v)={{νβ1(u)|uG1 and h(u)=v},whenh1(v)1,elsewhere.

    Also, h1(ψ2) is an (α,β)-Pythagorean fuzzy set on G1 and defined by

    h1(ψ2)(u)=(h1(μα2)(u),h1(νβ2)(u)) for all uG1, where

    (h1(μα2))(u)=(μα2(h(u)) and (h1(νβ2))(u)=(νβ2(h(u)).

    Example 4.1. We consider two groups G1=(Z,+) and G2=({1,1},.).

    Let h be a mapping from G1 to G2 defined by

    h(u)={1,if uiseven1,elsewhere.

    Let ψ1=(μα1,νβ1) and ψ2=(μα2,νβ2) be two (α,β)-Pythagorean fuzzy sets on G1 and G2 respectively, is given by

    μα1(u)={0.9,whenu2Z0.6,elsewhere
    νβ1(u)={0.2,whenu2Z0.7,elsewhere

    and μα2(1)=0.8, μα2(1)=0.4, νβ2(1)=0.5, νβ2(1)=0.6.

    Then h(ψ1) is an (α,β)-Pythagorean fuzzy set on G2 is given by h(μα1)(1)=0.9, h(μα1)(1)=0.6, h(νβ1)(1)=0.2 and h(νβ1)(1)=0.7.

    Also, h1(ψ2) is an (α,β)-Pythagorean fuzzy set on G1 is given by

    h1(μα2)(u)={0.8,whenu2Z0.4,elsewhere
    h1(νβ2)(u)={0.5,whenu2Z0.6,elsewhere.

    Theorem 4.1. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G1 and h be a group homomorphism from G1 onto G2. Then h(ψ) is an (α,β)-PFSG of the group G2.

    Proof. Since h:G1G2 is an onto homomorphism, therefore h(G1)=G2.

    Let u2 and v2 be two elements of G2.

    Suppose u2=h(u1) and v2=h(v1) for some u1, v1G1.

    We have h(ψ)(v)=(h(μα)(v),h(νβ)(v)) for all vG2. Now

    h(μα)(u2v2)={μα(w)|wG1,h(w)=u2v2}{μα(u1v1)|u1,v1G1 and h(u1)=u2,h(v1)=v2}{μα(u1)μα(v1)|u1,v1G1 and h(u1)=u2,h(v1)=v2}=({μα(u1)|u1G1 and h(u1)=u2})({μα(v1)|v1G1 and h(v1)=v2})=h(μα)(u2)h(μα)(v2).

    Therefore h(μα)(u2v2)h(μα)(u2)h(μα)(v2) for all u2 and v2G2.

    Similarly, we can prove that h(νβ)(u2v2)h(νβ)(u2)h(νβ)(v2) for all u2 and v2G2. Again

    h(μα)(u12)={μα(w)|wG1 and h(w)=(u12)}={μα(w1)|wG1 and h(w1)=(u2)}=h(μα)(u2).

    Therefore h(μα)(u12)=h(μα)(u2) for all u2G2.

    Similarly, we can show that h(νβ)(u12)=h(νβ)(u2) for all u2G2.

    Hence h(ψ)=(h(μα),h(νβ)) is an (α,β)-PFSG of the group G2.

    Theorem 4.2. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G2 and h be a bijective group homomorphism from G1 onto G2. Then h1(ψ) is an (α,β)-PFSG of the group G1.

    Proof. Let u1 and v1 be any two elements of G1.

    We have h1(ψ)(u)=(h1(μα)(u),h1(νβ)(u)) for all uG1. Now

    h1(μα)(u1v1)=μα(h(u1v1))=μα(h(u1)h(v1)) (Since h is a homomorphism)μα(h(u1))μα(h(v1))=h1(μα)(u1)h1(μα)(v1).

    Therefore h1(μα)(u1v1)h1(μα)(u1)h1(μα)(v1) for all u1 and v1G1.

    Similarly, we can show that h1(νβ)(u1v1)h1(νβ)(u1)h1(νβ)(v1) for all u1 and v1G1. Again

    h1(μα)(u11)=μα(h(u11))=μα(h(u1)1)=μα(h(u1))=h1(μα)(u1).

    Therefore h1(μα)(u11)=h1(μα)(u1) for all u1G1.

    Similarly, we can show that h1(νβ)(u11)=h1(νβ)(u1) for all u1G1.

    Hence h1(ψ)=(h1(μα),h1(νβ)) is an (α,β)-PFSG of the group G1.

    Definition 4.2. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then the (α,β)-Pythagorean fuzzy normalizer of ψ is denoted by (ψ) and defined by

    (ψ)={u| uG, μα(p)=μα(upu1) and νβ(u)=νβ(upu1) for all pG.

    Example 4.2. We consider the (α,β)-PFSG ψ1=(μα1,νβ1) of the group G1=(Z,+) in Example 4.1.

    Then the (α,β)-Pythagorean fuzzy normalizer of ψ is (ψ)=Z.

    Theorem 4.3. Let ψ=(μα,νβ) be an (α,β)-PFSG of a finite group G. Then (α,β)-Pythagorean fuzzy normalizer (ψ) is a subgroup of the group G.

    Proof. Let u and v be two elements of (ψ).

    Then we have

    μα(p)=μα(upu1), νβ(p)=νβ(upu1) pG (4.1)

    and

    μα(q)=μα(vqv1), νβ(q)=νβ(vqv1) qG. (4.2)

    Clearly e(ψ), so (ψ) is a non-empty finite subset of G.

    To show that (ψ) is a subgroup of G, it is enough to show uv(ψ).

    Now put p=vqv1 in (4.1), we have

    μα(vqv1)=μα(uvqv1u1) and νβ(vqv1)=νβ(uvqv1u1). (4.3)

    Then by applying (4.2) in (4.3), we get μα(q)=μα(uvqv1u1) and νβ(q)=νβ(uvqv1u1).

    This implies that μα(q)=μα((uv)q(uv)1) and νβ(q)=νβ((uv)q(uv)1).

    Therefore uv(ψ).

    Hence (ψ) is a subgroup of the group G.

    Proposition 4.1. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then ψ=(μα,νβ) is an (α,β)-PFNSG of the group G if and only if (ψ)=G.

    Proof. We have (ψ)={u| uG, μα(p)=μα(upu1) and νβ(u)=νβ(upu1) for all pG.

    Therefore (ψ)G.

    Let ψ=(μα,νβ) be a (α,β)-PFNSG of a group G.

    Then we have μα(u)=μα(vuv1) and νβ(u)=νβ(vuv1) for all u,vG.

    This shows that G(ψ).

    Hence (ψ)=G.

    Conversely, let (ψ)=G.

    Then μα(u)=μα(vuv1) and νβ(u)=νβ(vuv1) for all u,vG.

    Therefore ψ=(μα,νβ) is an (α,β)-PFNSG of the group G.

    Proposition 4.2. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then ψ is an (α,β)-PFNSG of the group (ψ).

    Proof. Let u and v be any elements of (ψ).

    Then μα(w)=μα(uwu1) and νβ(w)=νβ(uwu1) for all wG.

    Since (ψ) is a subgroup of the group G, then vu(ψ).

    Putting w=vu in the above relation we get

    μα(vu)=μα(uvuu1) and νβ(vu)=νβ(uvuu1).

    This implies that μα(vu)=μα(uv) and νβ(vu)=νβ(uv), which is a necessary condition for an (α,β)-PFSG to be an (α,β)-PFNSG of a group.

    Hence ψ is an (α,β)-PFNSG of the group (ψ).

    Definition 4.3. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then (α,β)-Pythagorean fuzzy centralizer of ψ is denoted by Cψ and defined by Cψ={u| uG, μα(uv)=μα(vu) and νβ(uv)=νβ(vu)} for all vG.

    Example 4.3. We consider the (α,β)-PFSG ψ of the Klein's 4-group V4 in Example 3.1.

    Then the (α,β)-Pythagorean fuzzy centralizer of ψ is Cψ=V4.

    Theorem 4.4. (α,β)-Pythagorean fuzzy centralizer of an (α,β)-PFSG of a group is a subgroup of that group.

    Proof. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G.

    Then (α,β)-Pythagorean fuzzy centralizer of ψ is given by

    Cψ={u| uG, μα(uv)=μα(vu) and νβ(uv)=νβ(vu)} for all vG.

    Let p and q be any two elements of Cψ. Now for any rG, we have

    μα((pq)r)=μα(p(qr))=μα((qr)p)=μα(q(rp))=μα((rp)q)=μα(r(pq)).

    Therefore μα((pq)r)=μα(r(pq)) for all rG.

    Similarly, we can show that νβ((pq)r)=νβ(r(pq)) for all rG.

    This shows that pqCψ. Again for any sG, we have

    μα(p1s)=μα((s1p)1)=μα(s1p)=μα(ps1)=μα((sp1)1)=μα(sp1).

    Therefore μα(p1s)=μα(sp1) for all sG.

    Similarly, we can prove that νβ(p1s)=νβ(sp1) for all sG.

    This proves that for pCψ, we have p1Cψ.

    Hence Cψ is a subgroup of the group G.

    In this section, we introduce the notion of (α,β)-Pythagorean fuzzy quotient group of an (α,β)-Pythagorean fuzzy subgroup and define the index of an (α,β)-Pythagorean fuzzy subgroup. We prove Lagrange's theorem for (α,β)-Pythagorean fuzzy subgroup.

    Theorem 5.1. Let ψ=(μα,νβ) be an (α,β)-PFNSG of a finite group G and Φ be the collection of all (α,β)-Pythagorean fuzzy cosets of ψ in G. Then Φ forms a group under the composition uψvψ=(uv)ψ for all u,vG.

    Proof. To show (Φ,) is a group under the composition uψvψ=(uv)ψ for all u,vG, first we have to show that this is a well defined binary operation.

    Let u,v,w,x be elements of G such that uψ=wψ and vψ=xψ.

    That is uμα(p)=wμα(p), uνβ(p)=wνβ(p) and vμα(p)=xμα(p), vνβ(p)=xνβ(p)pG.

    This implies that for all pG,

    μα(u1p)=μα(w1p), νβ(u1p)=νβ(w1p) (5.1)

    and

    μα(v1p)=μα(x1p), νβ(v1p)=νβ(x1p). (5.2)

    We have to show that uψvψ=wψxψ.

    That is (uv)ψ=(wx)ψ.

    We have (uv)μα(p)=μα(v1u1p) and (wx)μα(p)=μα(x1w1p)pG. Now

    μα(v1u1p)=μα(v1u1ww1p)=μα(v1u1wxx1w1p)μα(v1u1wx)μα(x1w1p).

    So,

    μα(v1u1p)μα(v1u1wx)μα(x1w1p) pG. (5.3)

    Putting p=u1wx in (5.2), we get

    μα(v1u1wx)=μα(x1u1wx).

    Since ψ=(μα,νβ) is an (α,β)-PFNSG of G, then μα(x1u1wx)=μα(u1w).

    Putting p=w in (5.1), we get

    μα(u1w)=μα(w1w)=μα(e).

    Consequently, μα(v1u1wx)=μα(e).

    So from (5.3), we have μα(v1u1p)μα(x1w1p).

    Similarly, we can show that μα(x1w1p)μα(v1u1p).

    Therefore μα(v1u1p)=μα(x1w1p), pG.

    Similarly, we can prove that νβ(v1u1p)=νβ(x1w1p), pG.

    This shows that, (uv)μα(p)=(wx)μα(p) and (uv)νβ(p)=(wx)νβ(p), pG.

    Consequently, (uv)ψ=(wx)ψ.

    Hence the composition is well defined on Φ.

    Since G is a finite group, then there is no ambiguity in closed and associativity of the composition on Φ.

    Clearly, eψ is the identity of Φ.

    Also, inverse of any element uψ of Φ is u1ψΦ.

    That is (uψ)(u1ψ)=eψ.

    Hence (Φ,) is a group under the composition uψvψ=(uv)ψ for all u,vG.

    Definition 5.1. Order of the group (Φ,), collection of all (α,β)-Pythagorean fuzzy cosets of an (α,β)-PFNSG ψ in a finite group G is called the index of ψ and denoted by [G:ψ].

    Example 5.1. Let us consider the group G=(Z3,+3), where '+3' is addition of integers modulo 3.

    Define the α-membership value and β-non-membership value of the elements of Z3 by

    μα(0)=0.8, μα(1)=0.7, μα(2)=0.7,νβ(0)=0.1, νβ(1)=0.2, νβ(2)=0.2.

    We can easily varify that ψ=(μα,νβ) is an (α,β)-PFNSG of the group G=(Z3,+3).

    Then the collection of all (α,β)-Pythagorean fuzzy cosets of ψ, Φ={0ψ,1ψ,2ψ}.

    Now (1μα)(1)=μα(11+31)=μα(2+31)=μα(0)=0.8 and (2μα)(1)=μα(21+31)=μα(1+31)=μα(2)=0.7.

    Therefore (1μα)(1)(2μα)(1). This shows that 1ψ2ψ.

    Hence the index of ψ, [G:ψ]=3.

    Theorem 5.2. Let ψ=(μα,νβ) be an (α,β)-PFNSG of a finite group G. Then an (α,β)-PFS Ψ=(μα,νβ) of Φ defined by μα(uμα)=μα(u) and νβ(uνβ)=νβ(u) is an (α,β)-PFSG of the group (Φ,) for all uG.

    Proof. Let uψ and vψ be any two elements of Φ, where u,vG. Now

    μα((uμα)(vμα))=μα((uv)μα)=μα(uv)μα(u)μα(v)=μα(uμα)μα(vμα).

    Therefore μα((uμα)(vμα))μα(uμα)μα(vμα).

    Similarly, we can show that νβ((uνβ)(vνβ))νβ(uνβ)νβ(vνβ).

    Again, μα(u1μα)=μα(u1)=μα(u)=μα(uμα).

    Similarly, we have νβ(u1νβ)=νβ(uνβ).

    Hence Ψ=(μα,νβ) is an (α,β)-PFSG of the group (Φ,).

    Definition 5.2. Let ψ=(μα,νβ) be an (α,β)-PFNSG of a finite group G. Then the (α,β)-PFSG Ψ=(μα,νβ) of the group (Φ,) is called (α,β)-Pythagorean fuzzy quotient group of ψ.

    Example 5.2. We consider the (α,β)-PFNSG ψ of the group (Z,+3) in Example 5.1.

    Therefore the collection of all (α,β)-Pythagorean fuzzy cosets of ψ, Φ={0ψ,1ψ,2ψ}.

    We define an (α,β)-PFS Ψ=(μα,νβ) of Φ by μα(uμα)=μα(u) and νβ(uνβ)=νβ(u).

    Then μα(0μα)=μα(0)=0.8, μα(1μα)=μα(1)=0.7, μα(2μα)=μα(2)=0.7 and

    νβ(0νβ)=νβ(0)=0.1, νβ(1νβ)=νβ(1)=0.2, νβ(2νβ)=νβ(2)=0.2.

    Now we can easily check that Ψ=(μα,νβ) is an (α,β)-PFSG of Φ.

    Therefore Ψ=(μα,νβ) is the (α,β)-Pythagorean fuzzy quotient group of ψ.

    Theorem 5.3. Let ψ=(μα,νβ) be an (α,β)-PFNSG of a finite group G and Φ be the collection of all (α,β)-Pythagorean fuzzy cosets of ψ in G. We define a mapping Δ:GΦ by Δ(u)=uψ for all uG. Then Δ is a group homomorphism from G to Φ with kernel (Δ)={uG| μα(u)=μα(e), νβ(u)=νβ(e).

    Proof. We have Δ:GΦ defined by Δ(u)=uψ for all uG.

    Let u and v be any elements of G. Then we have Δ(uv)=(uv)ψ=(uψ)(vψ)=Δ(u)Δ(v).

    This proves that Δ is a group homomorphism from G to Φ.

    Now the kernel of Δ is given by

    ker(Δ)={uG|Δ(u)=eψ}={uG|uψ=eψ}={uG|uψ(v)=eψ(v), vG}={uG|uμα(v)=eμα(v), uνβ(v)=eνβ(v), vG}={uG|μα(u1v)=μα(v), νβ(u1v)=νβ(v), vG}={uG|μα(u)=μα(e), νβ(u)=νβ(e)}.

    Hence ker(Δ)={uG|μα(u)=μα(e), νβ(u)=νβ(e)}.

    Remark 5.1. ker(Δ) is a subgroup of the group G.

    Theorem 5.4. (An approach to Lagrange's theorem in (α,β)-PFSG)

    Let ψ=(μα,νβ) be an (α,β)-PFNSG of a finite group G. Then [G:ψ] divides O(G).

    Proof. We have ψ=(μα,νβ) is an (α,β)-PFNSG of a finite group G.

    Then Φ={uψ|uG}, the collection of all (α,β)-PFC of ψ in G is also finite.

    In Theorem 5.3, We have seen that there is a group homomorphism Δ from G to Φ by Δ(u)=uψ for all uG.

    We set M={uG|uψ=eψ}.

    Then M=ker(Δ), which is a subgroup of G.

    Now, we decompose G as a disjoint union of left cosets of G modulo m as follows

    G=u1M  u2M  u3M umM

    where umM=M.

    We have to show that there is a one-one correspondence between cosets uiM of G and the elements of Φ.

    We consider any coset uiM of G and any element mM.

    Then we have Δ(uim)=uimψ=(uiψ)(mψ)=(uiψ)(eψ)=(uiψ).

    This shows that Δ maps every element of uiM to the (α,β)-PFC uiψ.

    Now, we construct a mapping Δ_ between {uiM| 1im} and Φ by Δ_(uiM)=uiψ.

    Let upψ=uqψ. Then we have u1qupψ=eψ.

    Therefore u1qupM.

    This implies that upM=uqM.

    Hence Δ_(uiM)=uiψ is a one-one mapping.

    Therefore we can conclude that the number of distinct cosets is equal to the cardinality of Φ.

    That is [G:M]=[G:ψ].

    Since [G:M] divides O(G), then [G:ψ] must divide O(G).

    Lagrange's theorem is a very useful theorem in finite group theory. Now we will give some applications of this theorem in (α,β)-Pythagorean fuzzy subgroups.

    Corollary 5.1. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then O(u)|(α,β)-PFO (u)ψ for all uG.

    Proof. Let u be any element of G and O(u)=k, where k is a positive integer.

    Then uk=e, where e is the identity element of G.

    We consider H=<u> is a subgroup of G.

    Now, μα(u2)μα(u)μα(u)=μα(u) and νβ(u2)νβ(u)νβ(u)=νβ(u).

    Thus by induction, we can show that μα(up)μα(u) and νβ(up)νβ(u) for all positive integer p.

    So, u, u2, , ukΩ(u). Consequently, HΩ(u).

    Therefore H is a subgroup of Ω(u).

    Thus by Lagrange's theorem, O(H)|O(Ω(u)).

    Therefore O(u)|(α,β)-PFO (u)ψ.

    Since u is an arbitrary element of G, O(u)|(α,β)-PFO (u)ψ for all uG.

    Corollary 5.2. Let ψ=(μα,νβ) be an (α,β)-PFSG of a group G. Then (α,β)-Pythagorean fuzzy order of each element of G in ψ divides the order of the group.

    Proof. According to the definition of (α,β)-Pythagorean fuzzy order of an element of G in ψ, (α,β)-PFO (u)ψ=O(Ω(u)) for all uG.

    From Theorem 3.1, Ω(u) is a subgroup of the group G.

    Therefore by Lagrange's theorem, the order of Ω(u) divides the order of the group (G,).

    That is O(Ω(u))|O(G).

    This implies that (α,β)-PFO (u)ψ|O(G) for all uG.

    Hence (α,β)-Pythagorean fuzzy order of each element of G in ψ divides the order of the group.

    The purpose of this paper is to explore the study of (α,β)-Pythagorean fuzzy subgroups. The whole paper revolved around the development of theories for fuzzification of Lagrange's theorem in (α,β)-Pythagorean fuzzy subgroups. All the sections of this paper are arranged in such a way that we can approach Lagrange's theorem. We have introduced the concept of relative subgroup of a group and defined the notion of (α,β)-Pythagorean fuzzy order of an element in (α,β)-PFSG. Various algebraic attributes of it are discussed. We have established a relation between order and (α,β)-Pythagorean fuzzy order of an element in a group and defined the extension principle for (α,β)-Pythagorean fuzzy sets. It is shown that homomorphic image and pre-image of an (α,β)-PFSG is also an (α,β)-PFSG. Further, the concept of (α,β)-Pythagorean fuzzy normalizer and (α,β)-Pythagorean fuzzy centralizer of an (α,β)-PFSG are given. We have proved that (α,β)-Pythagorean fuzzy normalizer and (α,β)-Pythagorean fuzzy centralizer of an (α,β)-PFSG are subgroups of that group. Moreover, we have introduced (α,β)-Pythagorean fuzzy quotient group and defined the index of an (α,β)-PFSG. Finally, we have presented the (α,β)-Pythagorean fuzzy version of Lagrange's theorem. We have produced some applications of Lagrange's theorem in (α,β)-Pythagorean fuzzy subgroups. In future, we will work on the number of (α,β)-Pythagorean fuzzy subgroups of a group.

    This research work of first author is sponsored by Council of Scientific and Industrial Research (CSIR), Human Resource Development Group (HRDG), INDIA. Sanctioned file no. is 09/599(0081)/2018-EMR-I. This work is partially supported by Research Council Faroe Islands and University of the Faroe Islands for the third author. The authors are grateful to the anonymous referees for a careful checking of the details and for helpful comments that improved the overall presentation of this paper.

    All authors declare that there is no conflict of interest.



    [1] A. Latif, A. Rasheed, U. Sajid, J. Ahmed, N. Ali, N. I. Ratyal, et al., Content-based image retrieval and feature extraction: a comprehensive review, Math. Probl. Eng., 2019.
    [2] B. Gupta, M. Tiwari, S. S. Lamba, Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement, CAAI Trans. Intell. Technol., 4 (2019), 73–79. doi: 10.1049/trit.2018.1006
    [3] S. Maheshwari, V. Kanhangad, R. B. Pachori, S. V. Bhandary, U. R. Acharya, Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques, Comput. Biol. Med., 105 (2019), 72–80. doi: 10.1016/j.compbiomed.2018.11.028
    [4] S. Masood, M. Sharif, M. Raza, M. Yasmin, M. Iqbal, M. Younus Javed, Glaucoma disease: A survey, Curr. Med. Imaging, 11 (2015), 272–283. doi: 10.2174/157340561104150727171246
    [5] U. R. Acharya, S. Bhat, J. E. Koh, S. V. Bhandary, H. Adeli, A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images, Comput. Biol. Med., 88 (2017), 72–83. doi: 10.1016/j.compbiomed.2017.06.022
    [6] B. J. Shingleton, L. S. Gamell, M. W. O'Donoghue, S. L. Baylus, R. King, Long-term changes in intraocular pressure after clear corneal phacoemulsification: Normal patients versus glaucoma suspect and glaucoma patients, J. Cataract. Refract. Surg., 25 (1999), 885–890. doi: 10.1016/S0886-3350(99)00107-8
    [7] K. F. Jamous, M. Kalloniatis, M. P. Hennessy, A. Agar, A. Hayen, B. Zangerl, Clinical model assisting with the collaborative care of glaucoma patients and suspects, Clin. Exp. Ophthalmol., 43 (2015), 308–319. doi: 10.1111/ceo.12466
    [8] T. Khalil, M. U. Akram, S. Khalid, S. H. Dar, N. Ali, A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage, Int. J. Imaging Syst. Technol., 8 (2021).
    [9] H. A. Quigley, A. T. Broman, The number of people with glaucoma worldwide in 2010 and 2020, Br. J. Ophthalmol., 90 (2006), 262–267. doi: 10.1136/bjo.2005.081224
    [10] C. Costagliola, R. Dell'Omo, M. R. Romano, M. Rinaldi, L. Zeppa, F. Parmeggiani, Pharmacotherapy of intraocular pressure: part i. parasympathomimetic, sympathomimetic and sympatholytics, Expert Opin. Pharmacother., 10 (2009), 2663–2677. doi: 10.1517/14656560903300103
    [11] A. A. Salam, M. U. Akram, K. Wazir, S. M. Anwar, M. Majid, Autonomous glaucoma detection from fundus image using cup to disc ratio and hybrid features, in ISSPIT.), IEEE, 2015,370–374.
    [12] R. JMJ, Leading causes of blindness worldwide, Bull. Soc. Belge. Ophtalmol., 283 (2002), 19–25.
    [13] M. K. Dutta, A. K. Mourya, A. Singh, M. Parthasarathi, R. Burget, K. Riha, Glaucoma detection by segmenting the super pixels from fundus colour retinal images, in 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom.), IEEE, 2014, 86–90.
    [14] C. E. Willoughby, D. Ponzin, S. Ferrari, A. Lobo, K. Landau, Y. Omidi, Anatomy and physiology of the human eye: effects of mucopolysaccharidoses disease on structure and function–a review, Clin. Exp. Ophthalmol., 38 (2010), 2–11.
    [15] M. S. Haleem, L. Han, J. Van Hemert, B. Li, Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review, Comput. Med. Imaging Graph., 37 (2013), 581–596. doi: 10.1016/j.compmedimag.2013.09.005
    [16] A. Sarhan, J. Rokne, R. Alhajj, Glaucoma detection using image processing techniques: A literature review, Comput. Med. Imaging Graph., 78 (2019), 101657. doi: 10.1016/j.compmedimag.2019.101657
    [17] H. A. Quigley, Neuronal death in glaucoma, Prog. Retin. Eye Res., 18 (1999), 39–57. doi: 10.1016/S1350-9462(98)00014-7
    [18] N. Salamat, M. M. S. Missen, A. Rashid, Diabetic retinopathy techniques in retinal images: A review, Artif. Intell. Med., 97 (2019), 168–188. doi: 10.1016/j.artmed.2018.10.009
    [19] F. Bokhari, T. Syedia, M. Sharif, M. Yasmin, S. L. Fernandes, Fundus image segmentation and feature extraction for the detection of glaucoma: A new approach, Curr. Med. Imaging Rev., 14 (2018), 77–87.
    [20] A. Agarwal, S. Gulia, S. Chaudhary, M. K. Dutta, R. Burget, K. Riha, Automatic glaucoma detection using adaptive threshold based technique in fundus image, in (TSP.), IEEE, 2015,416–420.
    [21] L. Xiong, H. Li, Y. Zheng, Automatic detection of glaucoma in retinal images, in 2014 9th IEEE Conference on Industrial Electronics and Applications, IEEE, 2014, 1016–1019.
    [22] A. Diaz-Pinto, S. Morales, V. Naranjo, T. Köhler, J. M. Mossi, A. Navea, Cnns for automatic glaucoma assessment using fundus images: An extensive validation, Biomed. Eng. Online, 18 (2019), 29. doi: 10.1186/s12938-019-0649-y
    [23] T. Kersey, C. I. Clement, P. Bloom, M. F. Cordeiro, New trends in glaucoma risk, diagnosis & management, Indian J. Med. Res., 137 (2013), 659.
    [24] A. L. Coleman, S. Miglior, Risk factors for glaucoma onset and progression, Surv. Ophthalmol., 53 (2008), S3–S10. doi: 10.1016/j.survophthal.2008.08.006
    [25] T. Saba, S. T. F. Bokhari, M. Sharif, M. Yasmin, M. Raza, Fundus image classification methods for the detection of glaucoma: A review, Microsc. Res. Tech., 81 (2018), 1105–1121. doi: 10.1002/jemt.23094
    [26] T. Aung, L. Ocaka, N. D. Ebenezer, A. G. Morris, M. Krawczak, D. L. Thiselton, et al., A major marker for normal tension glaucoma: association with polymorphisms in the opa1 gene, Hum. Genet., 110 (2002), 52–56. doi: 10.1007/s00439-001-0645-7
    [27] M. A. Khaimi, Canaloplasty: A minimally invasive and maximally effective glaucoma treatment, J. Ophthalmol., 2015.
    [28] M. Bechmann, M. J. Thiel, B. Roesen, S. Ullrich, M. W. Ulbig, K. Ludwig, Central corneal thickness determined with optical coherence tomography in various types of glaucoma, Br. J. Ophthalmol., 84 (2000), 1233–1237. doi: 10.1136/bjo.84.11.1233
    [29] D. Ahram, W. Alward, M. Kuehn, The genetic mechanisms of primary angle closure glaucoma, Eye., 29 (2015), 1251–1259. doi: 10.1038/eye.2015.124
    [30] R. Törnquist, Chamber depth in primary acute glaucoma, Br. J. Ophthalmol., 40 (1956), 421. doi: 10.1136/bjo.40.7.421
    [31] H. S. Sugar, F. A. Barbour, Pigmentary glaucoma*: A rare clinical entity, Am. J. Ophthalmol., 32 (1949), 90–92. doi: 10.1016/0002-9394(49)91112-5
    [32] H. S. Sugar, Pigmentary glaucoma: A 25-year review, Am. J. Ophthalmol., 62 (1966), 499–507. doi: 10.1016/0002-9394(66)91330-4
    [33] R. Ritch, U. Schlötzer-Schrehardt, A. G. Konstas, Why is glaucoma associated with exfoliation syndrome?, Prog. Retin. Eye Res., 22 (2003), 253–275. doi: 10.1016/S1350-9462(02)00014-9
    [34] J. L.-O. De, C. A. Girkin, Ocular trauma-related glaucoma., Ophthalmol. Clin. North. Am., 15 (2002), 215–223. doi: 10.1016/S0896-1549(02)00011-1
    [35] E. Milder, K. Davis, Ocular trauma and glaucoma, Int. Ophthalmol. Clin., 48 (2008), 47–64. doi: 10.1097/IIO.0b013e318187fcb8
    [36] T. G. Papadaki, I. P. Zacharopoulos, L. R. Pasquale, W. B. Christen, P. A. Netland, C. S. Foster, Long-term results of ahmed glaucoma valve implantation for uveitic glaucoma, Am. J. Ophthalmol., 144 (2007), 62–69. doi: 10.1016/j.ajo.2007.03.013
    [37] H. C. Laganowski, M. G. K. Muir, R. A. Hitchings, Glaucoma and the iridocorneal endothelial syndrome, Arch. Ophthalmol., 110 (1992), 346–350. doi: 10.1001/archopht.1992.01080150044025
    [38] C. L. Ho, D. S. Walton, Primary congenital glaucoma: 2004 update, J. Pediatr. Ophthalmol. Strabismus., 41 (2004), 271–288. doi: 10.3928/01913913-20040901-11
    [39] M. Erdurmuş, R. Yağcı, Ö. Atış, R. Karadağ, A. Akbaş, İ. F. Hepşen, Antioxidant status and oxidative stress in primary open angle glaucoma and pseudoexfoliative glaucoma, Curr. Eye Res., 36 (2011), 713–718. doi: 10.3109/02713683.2011.584370
    [40] S. S. Hayreh, Neovascular glaucoma, Prog. Retin. Eye Res., 26 (2007), 470–485. doi: 10.1016/j.preteyeres.2007.06.001
    [41] D. A. Lee, E. J. Higginbotham, Glaucoma and its treatment: a review, Am. J. Health. Syst. Pharm., 62 (2005), 691–699. doi: 10.1093/ajhp/62.7.691
    [42] X. Wang, R. Khan, A. Coleman, Device-modified trabeculectomy for glaucoma, Cochrane. Database Syst. Rev..
    [43] K. R. Sung, J. S. Kim, G. Wollstein, L. Folio, M. S. Kook, J. S. Schuman, Imaging of the retinal nerve fibre layer with spectral domain optical coherence tomography for glaucoma diagnosis, Br. J. Ophthalmol., 95 (2011), 909–914. doi: 10.1136/bjo.2010.186924
    [44] M. E. Karlen, E. Sanchez, C. C. Schnyder, M. Sickenberg, A. Mermoud, Deep sclerectomy with collagen implant: medium term results, Br. J. Ophthalmol., 83 (1999), 6–11. doi: 10.1136/bjo.83.1.6
    [45] J. Carrillo, L. Bautista, J. Villamizar, J. Rueda, M. Sanchez, D. Rueda, Glaucoma detection using fundus images of the eye, 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), IEEE, 2019, 1–4.
    [46] N. Sengar, M. K. Dutta, R. Burget, M. Ranjoha, Automated detection of suspected glaucoma in digital fundus images, 2017 40th International Conference on Telecommunications and Signal Processing (TSP), IEEE, 2017,749–752.
    [47] A. Poshtyar, J. Shanbehzadeh, H. Ahmadieh, Automatic measurement of cup to disc ratio for diagnosis of glaucoma on retinal fundus images, in 2013 6th International Conference on Biomedical Engineering and Informatics, IEEE, 2013, 24–27.
    [48] F. Khan, S. A. Khan, U. U. Yasin, I. ul Haq, U. Qamar, Detection of glaucoma using retinal fundus images, in The 6th 2013 Biomedical Engineering International Conference, IEEE, 2013, 1–5.
    [49] H. Yamada, T. Akagi, H. Nakanishi, H. O. Ikeda, Y. Kimura, K. Suda, et al., Microstructure of peripapillary atrophy and subsequent visual field progression in treated primary open-angle glaucoma, Ophthalmology, 123 (2016), 542–551. doi: 10.1016/j.ophtha.2015.10.061
    [50] J. B. Jonas, Clinical implications of peripapillary atrophy in glaucoma, Curr. Opin. Ophthalmol., 16 (2005), 84–88. doi: 10.1097/01.icu.0000156135.20570.30
    [51] K. H. Park, G. Tomita, S. Y. Liou, Y. Kitazawa, Correlation between peripapillary atrophy and optic nerve damage in normal-tension glaucoma, Ophthalmol., 103 (1996), 1899–1906. doi: 10.1016/S0161-6420(96)30409-0
    [52] F. A. Medeiros, L. M. Zangwill, C. Bowd, R. M. Vessani, R. Susanna Jr, R. N. Weinreb, Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography, Am. J. Ophthalmol., 139 (2005), 44–55. doi: 10.1016/j.ajo.2004.08.069
    [53] G. Wollstein, J. S. Schuman, L. L. Price, A. Aydin, P. C. Stark, E. Hertzmark, et al., Optical coherence tomography longitudinal evaluation of retinal nerve fiber layer thickness in glaucoma, Arch. Ophthalmol., 123 (2005), 464–470. doi: 10.1001/archopht.123.4.464
    [54] M. Armaly, The optic cup in the normal eye: I. cup width, depth, vessel displacement, ocular tension and outflow facility, Am. J. Ophthalmol., 68 (1969), 401–407. doi: 10.1016/0002-9394(69)90702-8
    [55] M. Galdos, A. Bayon, F. D. Rodriguez, C. Mico, S. C. Sharma, E. Vecino, Morphology of retinal vessels in the optic disk in a göttingen minipig experimental glaucoma model, Vet. Ophthalmol., 15 (2012), 36–46.
    [56] W. Zhou, Y. Yi, Y. Gao, J. Dai, Optic disc and cup segmentation in retinal images for glaucoma diagnosis by locally statistical active contour model with structure prior, Comput. Math. Methods. Med., 2019.
    [57] P. Sharma, P. A. Sample, L. M. Zangwill, J. S. Schuman, Diagnostic tools for glaucoma detection and management, Surv. Ophthalmol., 53 (2008), S17–S32. doi: 10.1016/j.survophthal.2008.08.003
    [58] M. J. Greaney, D. C. Hoffman, D. F. Garway-Heath, M. Nakla, A. L. Coleman, J. Caprioli, Comparison of optic nerve imaging methods to distinguish normal eyes from those with glaucoma, Invest. Ophthalmol. Vis. Sci., 43 (2002), 140–145.
    [59] R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, G. Michelson, Glaucoma risk index: automated glaucoma detection from color fundus images, Med. Image. Anal., 14 (2010), 471–481. doi: 10.1016/j.media.2009.12.006
    [60] K. Chan, T.-W. Lee, P. A. Sample, M. H. Goldbaum, R. N. Weinreb, T. J. Sejnowski, Comparison of machine learning and traditional classifiers in glaucoma diagnosis, IEEE Trans. Biomed. Eng., 49 (2002), 963–974. doi: 10.1109/TBME.2002.802012
    [61] N. Varachiu, C. Karanicolas, M. Ulieru, Computational intelligence for medical knowledge acquisition with application to glaucoma, in Proceedings First IEEE International Conference on Cognitive Informatics, IEEE, 2002,233–238.
    [62] J. Yu, S. S. R. Abidi, P. H. Artes, A. McIntyre, M. Heywood, Automated optic nerve analysis for diagnostic support inglaucoma, in CBMS'05., IEEE, 2005, 97–102.
    [63] R. Bock, J. Meier, G. Michelson, L. G. Nyúl and J. Hornegger, Classifying glaucoma with image-based features from fundus photographs, in Joint Pattern Recognition Symposium., Springer, 2007,355–364.
    [64] Y. Hatanaka, A. Noudo, C. Muramatsu, A. Sawada, T. Hara, T. Yamamoto, et al., Vertical cup-to-disc ratio measurement for diagnosis of glaucoma on fundus images, in Medical Imaging 2010: Computer-Aided Diagnosis, vol. 7624, International Society for Optics and Photonics, 2010, 76243C.
    [65] S. S. Abirami, S. G. Shoba, Glaucoma images classification using fuzzy min-max neural network based on data-core, IJISME., 1 (2013), 9–15.
    [66] J. Liu, Z. Zhang, D. W. K. Wong, Y. Xu, F. Yin, J. Cheng, et al., Automatic glaucoma diagnosis through medical imaging informatics, Journal of the American Medical Informatics Association, 20 (2013), 1021–1027. doi: 10.1136/amiajnl-2012-001336
    [67] A. Almazroa, R. Burman, K. Raahemifar, V. Lakshminarayanan, Optic disc and optic cup segmentation methodologies for glaucoma image detection: A survey, J. Ophthalmol., 2015.
    [68] A. Dey, S. K. Bandyopadhyay, Automated glaucoma detection using support vector machine classification method, J. Adv. Med. Med. Res., 1–12.
    [69] F. R. Silva, V. G. Vidotti, F. Cremasco, M. Dias, E. S. Gomi, V. P. Costa, Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using spectral domain oct and standard automated perimetry, Arq. Bras. Oftalmol., 76 (2013), 170–174. doi: 10.1590/S0004-27492013000300008
    [70] M. U. Akram, A. Tariq, S. Khalid, M. Y. Javed, S. Abbas, U. U. Yasin, Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques, Australas. Phys. Eng. Sci. Med., 38 (2015), 643–655. doi: 10.1007/s13246-015-0377-y
    [71] A. T. A. Al-Sammarraie, R. R. Jassem, T. K. Ibrahim, Mixed convection heat transfer in inclined tubes with constant heat flux, Eur. J. Sci. Res., 97 (2013), 144–158.
    [72] M. R. K. Mookiah, U. R. Acharya, C. M. Lim, A. Petznick, J. S. Suri, Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features, Knowl. Based. Syst., 33 (2012), 73–82. doi: 10.1016/j.knosys.2012.02.010
    [73] C. Raja, N. Gangatharan, Glaucoma detection in fundal retinal images using trispectrum and complex wavelet-based features, Eur. J. Sci. Res., 97 (2013), 159–171.
    [74] G. Lim, Y. Cheng, W. Hsu, M. L. Lee, Integrated optic disc and cup segmentation with deep learning, in ICTAI., IEEE, 2015,162–169.
    [75] K.-K. Maninis, J. Pont-Tuset, P. Arbeláez, L. Van Gool, Deep retinal image understanding, in International conference on medical image computing and computer-assisted intervention, Springer, 2016,140–148.
    [76] B. Al-Bander, W. Al-Nuaimy, B. M. Williams, Y. Zheng, Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc, Biomed. Signal Process Control., 40 (2018), 91–101. doi: 10.1016/j.bspc.2017.09.008
    [77] A. Mitra, P. S. Banerjee, S. Roy, S. Roy, S. K. Setua, The region of interest localization for glaucoma analysis from retinal fundus image using deep learning, Comput. Methods Programs Biomed., 165 (2018), 25–35. doi: 10.1016/j.cmpb.2018.08.003
    [78] S. S. Kruthiventi, K. Ayush, R. V. Babu, Deepfix: A fully convolutional neural network for predicting human eye fixations, IEEE Trans. Image. Process., 26 (2017), 4446–4456. doi: 10.1109/TIP.2017.2710620
    [79] M. Norouzifard, A. Nemati, A. Abdul-Rahman, H. GholamHosseini, R. Klette, A comparison of transfer learning techniques, deep convolutional neural network and multilayer neural network methods for the diagnosis of glaucomatous optic neuropathy, in International Computer Symposium, Springer, 2018,627–635.
    [80] X. Sun, Y. Xu, W. Zhao, T. You, J. Liu, Optic disc segmentation from retinal fundus images via deep object detection networks, in EMBC., IEEE, 2018, 5954–5957.
    [81] Z. Ghassabi, J. Shanbehzadeh, K. Nouri-Mahdavi, A unified optic nerve head and optic cup segmentation using unsupervised neural networks for glaucoma screening, in EMBC., IEEE, 2018, 5942–5945.
    [82] J. H. Tan, S. V. Bhandary, S. Sivaprasad, Y. Hagiwara, A. Bagchi, U. Raghavendra, et al., Age-related macular degeneration detection using deep convolutional neural network, Future Gener. Comput. Syst., 87 (2018), 127–135. doi: 10.1016/j.future.2018.05.001
    [83] J. Zilly, J. M. Buhmann, D. Mahapatra, Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation, Comput. Med. Imaging Graph., 55 (2017), 28–41. doi: 10.1016/j.compmedimag.2016.07.012
    [84] J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, et al., Superpixel classification based optic disc and optic cup segmentation for glaucoma screening, IEEE Trans. Med. Imaging, 32 (2013), 1019–1032. doi: 10.1109/TMI.2013.2247770
    [85] H. Ahmad, A. Yamin, A. Shakeel, S. O. Gillani, U. Ansari, Detection of glaucoma using retinal fundus images, in iCREATE., IEEE, 2014,321–324.
    [86] S. Kavitha, S. Karthikeyan, K. Duraiswamy, Neuroretinal rim quantification in fundus images to detect glaucoma, IJCSNS., 10 (2010), 134–140.
    [87] Z. Zhang, B. H. Lee, J. Liu, D. W. K. Wong, N. M. Tan, J. H. Lim, et al., Optic disc region of interest localization in fundus image for glaucoma detection in argali, in 2010 5th IEEE Conference on Industrial Electronics and Applications, IEEE, 2010, 1686–1689.
    [88] D. Welfer, J. Scharcanski, C. M. Kitamura, M. M. Dal Pizzol, L. W. Ludwig, D. R. Marinho, Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach, Computers Biol. Med., 40 (2010), 124–137. doi: 10.1016/j.compbiomed.2009.11.009
    [89] H. Tjandrasa, A. Wijayanti, N. Suciati, Optic nerve head segmentation using hough transform and active contours, Telkomnika, 10 (2012), 531. doi: 10.12928/telkomnika.v10i3.833
    [90] M. Tavakoli, M. Nazar, A. Golestaneh, F. Kalantari, Automated optic nerve head detection based on different retinal vasculature segmentation methods and mathematical morphology, in NSS/MIC., IEEE, 2017, 1–7.
    [91] P. Bibiloni, M. González-Hidalgo, S. Massanet, A real-time fuzzy morphological algorithm for retinal vessel segmentation, J. Real Time Image Process., 16 (2019), 2337–2350. doi: 10.1007/s11554-018-0748-1
    [92] A. Agarwal, A. Issac, A. Singh, M. K. Dutta, Automatic imaging method for optic disc segmentation using morphological techniques and active contour fitting, in 2016 Ninth International Conference on Contemporary Computing (IC3), IEEE, 2016, 1–5.
    [93] S. Pal, S. Chatterjee, Mathematical morphology aided optic disk segmentation from retinal images, in 2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), IEEE, 2017,380–385.
    [94] L. Wang, A. Bhalerao, Model based segmentation for retinal fundus images, in Scandinavian Conference on Image Analysis, Springer, 2003,422–429.
    [95] G. Deng, L. Cahill, An adaptive gaussian filter for noise reduction and edge detection, in 1993 IEEE conference record nuclear science symposium and medical imaging conference, IEEE, 1993, 1615–1619.
    [96] K. A. Vermeer, F. M. Vos, H. G. Lemij, A. M. Vossepoel, A model based method for retinal blood vessel detection, Comput. Biol. Med., 34 (2004), 209–219. doi: 10.1016/S0010-4825(03)00055-6
    [97] J. I. Orlando, E. Prokofyeva, M. B. Blaschko, A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images, IEEE Trans. Biomed. Eng., 64 (2016), 16–27.
    [98] R. Ingle, P. Mishra, Cup segmentation by gradient method for the assessment of glaucoma from retinal image, Int. J. Latest Trends. Eng. Technol., 4 (2013), 2540–2543.
    [99] G. D. Joshi, J. Sivaswamy, S. Krishnadas, Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment, IEEE Trans. Biomed. Eng., 30 (2011), 1192–1205.
    [100] W. W. K. Damon, J. Liu, T. N. Meng, Y. Fengshou, W. T. Yin, Automatic detection of the optic cup using vessel kinking in digital retinal fundus images, in 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), IEEE, 2012, 1647–1650.
    [101] D. Finkelstein, Kinks, J. Math. Phys., 7 (1966), 1218–1225.
    [102] Y. Xu, L. Duan, S. Lin, X. Chen, D. W. K. Wong, T. Y. Wong, et al., Optic cup segmentation for glaucoma detection using low-rank superpixel representation, in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2014,788–795.
    [103] Y. Xu, J. Liu, S. Lin, D. Xu, C. Y. Cheung, T. Aung, et al., Efficient optic cup detection from intra-image learning with retinal structure priors, in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2012, 58–65.
    [104] M. A. Aslam, M. N. Salik, F. Chughtai, N. Ali, S. H. Dar, T. Khalil, Image classification based on mid-level feature fusion, in 2019 15th International Conference on Emerging Technologies (ICET), IEEE, 2019, 1–6.
    [105] N. Ali, K. B. Bajwa, R. Sablatnig, S. A. Chatzichristofis, Z. Iqbal, M. Rashid, et al., A novel image retrieval based on visual words integration of sift and surf, PloS. one., 11 (2016), e0157428. doi: 10.1371/journal.pone.0157428
    [106] C.-Y. Ho, T.-W. Pai, H.-T. Chang, H.-Y. Chen, An atomatic fundus image analysis system for clinical diagnosis of glaucoma, in 2011 International Conference on Complex, Intelligent, and Software Intensive Systems, IEEE, 2011,559–564.
    [107] H.-T. Chang, C.-H. Liu, T.-W. Pai, Estimation and extraction of b-cell linear epitopes predicted by mathematical morphology approaches, J. Mol. Recognit., 21 (2008), 431–441. doi: 10.1002/jmr.910
    [108] D. Wong, J. Liu, J. Lim, N. Tan, Z. Zhang, S. Lu, et al., Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in argali, in 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2009, 5777–5780.
    [109] F. Yin, J. Liu, D. W. K. Wong, N. M. Tan, C. Cheung, M. Baskaran, et al., Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis, in 2012 25th IEEE international symposium on computer-based medical systems (CBMS), IEEE, 2012, 1–6.
    [110] S. Chandrika, K. Nirmala, Analysis of cdr detection for glaucoma diagnosis, IJERA., 2 (2013), 23–27.
    [111] N. Annu, J. Justin, Automated classification of glaucoma images by wavelet energy features, IJERA., 5 (2013), 1716–1721.
    [112] H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, X. Cao, Joint optic disc and cup segmentation based on multi-label deep network and polar transformation, IEEE Trans. Med. Imaging., 37 (2018), 1597–1605. doi: 10.1109/TMI.2018.2791488
    [113] P. K. Dhar, T. Shimamura, Blind svd-based audio watermarking using entropy and log-polar transformation, JISA., 20 (2015), 74–83.
    [114] D. Wong, J. Liu, J. Lim, H. Li, T. Wong, Automated detection of kinks from blood vessels for optic cup segmentation in retinal images, in Medical Imaging 2009: Computer-Aided Diagnosis, vol. 7260, International Society for Optics and Photonics, 2009, 72601J.
    [115] A. Murthi, M. Madheswaran, Enhancement of optic cup to disc ratio detection in glaucoma diagnosis, in 2012 International Conference on Computer Communication and Informatics, IEEE, 2012, 1–5.
    [116] N. E. A. Khalid, N. M. Noor, N. M. Ariff, Fuzzy c-means (fcm) for optic cup and disc segmentation with morphological operation, Procedia. Comput. Sci., 42 (2014), 255–262. doi: 10.1016/j.procs.2014.11.060
    [117] H. A. Nugroho, W. K. Oktoeberza, A. Erasari, A. Utami, C. Cahyono, Segmentation of optic disc and optic cup in colour fundus images based on morphological reconstruction, in 2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE), IEEE, 2017, 1–5.
    [118] L. Zhang, M. Fisher, W. Wang, Retinal vessel segmentation using multi-scale textons derived from keypoints, Comput. Med. Imaging Graph., 45 (2015), 47–56. doi: 10.1016/j.compmedimag.2015.07.006
    [119] X. Li, B. Aldridge, R. Fisher, J. Rees, Estimating the ground truth from multiple individual segmentations incorporating prior pattern analysis with application to skin lesion segmentation, in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE, 2011, 1438–1441.
    [120] M. M. Fraz, P. Remagnino, A. Hoppe, S. Velastin, B. Uyyanonvara, S. Barman, A supervised method for retinal blood vessel segmentation using line strength, multiscale gabor and morphological features, in 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), IEEE, 2011,410–415.
    [121] M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, M. D. Abramoff, Comparative study of retinal vessel segmentation methods on a new publicly available database, in Medical imaging 2004: Image processing, vol. 5370, International Society for Optics and Photonics, 2004,648–656.
    [122] J. Y. Choi, T. K. Yoo, J. G. Seo, J. Kwak, T. T. Um, T. H. Rim, Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database, PloS one, 12.
    [123] L. Zhang, M. Fisher, W. Wang, Comparative performance of texton based vascular tree segmentation in retinal images, in 2014 IEEE International Conference on Image Processing (ICIP), IEEE, 2014,952–956.
    [124] A. Septiarini, A. Harjoko, R. Pulungan, R. Ekantini, Automated detection of retinal nerve fiber layer by texture-based analysis for glaucoma evaluation, Healthc. Inform. Res., 24 (2018), 335–345. doi: 10.4258/hir.2018.24.4.335
    [125] B. S. Kirar, D. K. Agrawal, Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images, IET Image Process., 13 (2018), 73–82.
    [126] K. Nirmala, N. Venkateswaran, C. V. Kumar, J. S. Christobel, Glaucoma detection using wavelet based contourlet transform, in 2017 International Conference on Intelligent Computing and Control (I2C2), IEEE, 2017, 1–5.
    [127] A. A. G. Elseid, A. O. Hamza, Glaucoma detection using retinal nerve fiber layer texture features, J. Clin. Eng., 44 (2019), 180–185. doi: 10.1097/JCE.0000000000000361
    [128] M. Claro, L. Santos, W. Silva, F. Araújo, N. Moura, A. Macedo, Automatic glaucoma detection based on optic disc segmentation and texture feature extraction, CLEI Electron. J., 19 (2016), 5.
    [129] L. Abdel-Hamid, Glaucoma detection from retinal images using statistical and textural wavelet features, J. Digit Imaging., 1–8.
    [130] S. Maetschke, B. Antony, H. Ishikawa, G. Wollstein, J. Schuman, R. Garnavi, A feature agnostic approach for glaucoma detection in oct volumes, PloS. one., 14 (2019), e0219126. doi: 10.1371/journal.pone.0219915
    [131] D. C. Hood, A. S. Raza, On improving the use of oct imaging for detecting glaucomatous damage, Br. J. Ophthalmol., 98 (2014), ii1–ii9. doi: 10.1136/bjophthalmol-2014-305156
    [132] D. C. Hood, Improving our understanding, and detection, of glaucomatous damage: an approach based upon optical coherence tomography (oct), Prog.Retin. Eye Res., 57 (2017), 46–75. doi: 10.1016/j.preteyeres.2016.12.002
    [133] H. S. Basavegowda, G. Dagnew, Deep learning approach for microarray cancer data classification, CAAI Trans. Intell. Technol., 5 (2020), 22–33. doi: 10.1049/trit.2019.0028
    [134] X. Chen, Y. Xu, D. W. K. Wong, T. Y. Wong, J. Liu, Glaucoma detection based on deep convolutional neural network, in 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, 2015,715–718.
    [135] U. T. Nguyen, A. Bhuiyan, L. A. Park, K. Ramamohanarao, An effective retinal blood vessel segmentation method using multi-scale line detection, Pattern Recognit., 46 (2013), 703–715. doi: 10.1016/j.patcog.2012.08.009
    [136] J. H. Tan, U. R. Acharya, S. V. Bhandary, K. C. Chua, S. Sivaprasad, Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network, J. Comput. Sci., 20 (2017), 70–79. doi: 10.1016/j.jocs.2017.02.006
    [137] Y. Chai, H. Liu, J. Xu, Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models, Knowl. Based Syst., 161 (2018), 147–156. doi: 10.1016/j.knosys.2018.07.043
    [138] A. Pal, M. R. Moorthy, A. Shahina, G-eyenet: A convolutional autoencoding classifier framework for the detection of glaucoma from retinal fundus images, in 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, 2018, 2775–2779.
    [139] R. Asaoka, M. Tanito, N. Shibata, K. Mitsuhashi, K. Nakahara, Y. Fujino, et al., Validation of a deep learning model to screen for glaucoma using images from different fundus cameras and data augmentation, Ophthalmol. Glaucoma., 2 (2019), 224–231. doi: 10.1016/j.ogla.2019.03.008
    [140] X. Chen, Y. Xu, S. Yan, D. W. K. Wong, T. Y. Wong, J. Liu, Automatic feature learning for glaucoma detection based on deep learning, in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015,669–677.
    [141] A. Singh, S. Sengupta, V. Lakshminarayanan, Glaucoma diagnosis using transfer learning methods, in Applications of Machine Learning, vol. 11139, International Society for Optics and Photonics, 2019, 111390U.
    [142] S. Maheshwari, V. Kanhangad, R. B. Pachori, Cnn-based approach for glaucoma diagnosis using transfer learning and lbp-based data augmentation, arXiv. preprint.
    [143] S. Sengupta, A. Singh, H. A. Leopold, T. Gulati, V. Lakshminarayanan, Ophthalmic diagnosis using deep learning with fundus images–a critical review, Artif. Intell. Med., 102 (2020), 101758. doi: 10.1016/j.artmed.2019.101758
    [144] U. Raghavendra, H. Fujita, S. V. Bhandary, A. Gudigar, J. H. Tan, U. R. Acharya, Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images, Inf. Sci., 441 (2018), 41–49. doi: 10.1016/j.ins.2018.01.051
    [145] R. Asaoka, H. Murata, A. Iwase, M. Araie, Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier, Ophthalmol., 123 (2016), 1974–1980. doi: 10.1016/j.ophtha.2016.05.029
    [146] Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, M. He, Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs, Ophthalmol., 125 (2018), 1199–1206. doi: 10.1016/j.ophtha.2018.01.023
    [147] V. V. Raghavan, V. N. Gudivada, V. Govindaraju, C. R. Rao, Cognitive computing: Theory and applications, Elsevier., 2016.
    [148] J. Sivaswamy, S. Krishnadas, G. D. Joshi, M. Jain, A. U. S. Tabish, Drishti-gs: Retinal image dataset for optic nerve head (onh) segmentation, in 2014 IEEE 11th international symposium on biomedical imaging (ISBI), IEEE, 2014, 53–56.
    [149] A. Chakravarty, J. Sivaswamy, Glaucoma classification with a fusion of segmentation and image-based features, in 2016 IEEE 13th international symposium on biomedical imaging (ISBI), IEEE, 2016,689–692.
    [150] E. Decencière, X. Zhang, G. Cazuguel, B. Lay, B. Cochener, C. Trone, et al., Feedback on a publicly distributed image database: The messidor database, Image Analys. Stereol., 33 (2014), 231–234. doi: 10.5566/ias.1155
    [151] A. Allam, A. Youssif, A. Ghalwash, Automatic segmentation of optic disc in eye fundus images: a survey, ELCVIA, 14 (2015), 1–20. doi: 10.5565/rev/elcvia.762
    [152] P. Porwal, S. Pachade, R. Kamble, M. Kokare, G. Deshmukh, V. Sahasrabuddhe, et al., Indian diabetic retinopathy image dataset (idrid): A database for diabetic retinopathy screening research, Data, 3 (2018), 25. doi: 10.3390/data3030025
    [153] F. Calimeri, A. Marzullo, C. Stamile, G. Terracina, Optic disc detection using fine tuned convolutional neural networks, in 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), IEEE, 2016, 69–75.
    [154] M. N. Reza, Automatic detection of optic disc in color fundus retinal images using circle operator, Biomed. Signal. Process. Control., 45 (2018), 274–283. doi: 10.1016/j.bspc.2018.05.027
    [155] Z. Zhang, F. S. Yin, J. Liu, W. K. Wong, N. M. Tan, B. H. Lee, et al., Origa-light: An online retinal fundus image database for glaucoma analysis and research, in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, 2010, 3065–3068.
    [156] Z. Zhang, J. Liu, F. Yin, B.-H. Lee, D. W. K. Wong, K. R. Sung, Achiko-k: Database of fundus images from glaucoma patients, in 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), IEEE, 2013,228–231.
    [157] F. Yin, J. Liu, D. W. K. Wong, N. M. Tan, B. H. Lee, J. Cheng, et al., Achiko-i retinal fundus image database and its evaluation on cup-to-disc ratio measurement, in 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), IEEE, 2013,224–227.
    [158] F. Fumero, S. Alayón, J. L. Sanchez, J. Sigut, M. Gonzalez-Hernandez, Rim-one: An open retinal image database for optic nerve evaluation, in 2011 24th international symposium on computer-based medical systems (CBMS), IEEE, 2011, 1–6.
    [159] J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, et al., Optic nerve head segmentation, IEEE Trans. Med. Imaging., 23 (2004), 256–264. doi: 10.1109/TMI.2003.823261
    [160] C. C. Sng, L.-L. Foo, C.-Y. Cheng, J. C. Allen Jr, M. He, G. Krishnaswamy, et al., Determinants of anterior chamber depth: the singapore chinese eye study, Ophthalmol., 119 (2012), 1143–1150. doi: 10.1016/j.ophtha.2012.01.011
    [161] Z. Zhang, J. Liu, C. K. Kwoh, X. Sim, W. T. Tay, Y. Tan, et al., Learning in glaucoma genetic risk assessment, in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, 2010, 6182–6185.
    [162] D. Wong, J. Liu, J. Lim, X. Jia, F. Yin, H. Li, et al., Level-set based automatic cup-to-disc ratio determination using retinal fundus images in argali, in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2008, 2266–2269.
    [163] T. Ge, L. Cui, B. Chang, Z. Sui, F. Wei, M. Zhou, Seri: A dataset for sub-event relation inference from an encyclopedia, in CCF International Conference on Natural Language Processing and Chinese Computing, Springer, 2018,268–277.
    [164] M. Haloi, Improved microaneurysm detection using deep neural networks, arXiv. preprint..
    [165] B. Antal, A. Hajdu, An ensemble-based system for automatic screening of diabetic retinopathy, Knowl. Based Syst., 60 (2014), 20–27. doi: 10.1016/j.knosys.2013.12.023
    [166] J. Nayak, R. Acharya, P. S. Bhat, N. Shetty, T.-C. Lim, Automated diagnosis of glaucoma using digital fundus images, J. Med. Syst., 33 (2009), 337. doi: 10.1007/s10916-008-9195-z
    [167] J. V. Soares, J. J. Leandro, R. M. Cesar, H. F. Jelinek, M. J. Cree, Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification, IEEE Trans. Med. Imaging., 25 (2006), 1214–1222. doi: 10.1109/TMI.2006.879967
    [168] S. Kankanahalli, P. M. Burlina, Y. Wolfson, D. E. Freund, N. M. Bressler, Automated classification of severity of age-related macular degeneration from fundus photographs, Invest. Ophthalmol. Vis. Sci., 54 (2013), 1789–1796. doi: 10.1167/iovs.12-10928
    [169] K. Prasad, P. Sajith, M. Neema, L. Madhu, P. Priya, Multiple eye disease detection using deep neural network, in TENCON 2019-2019 IEEE Region 10 Conference (TENCON), IEEE, 2019, 2148–2153.
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