Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology

Xinlin Liu; Viktor Krylov; Su Jun; Natalya Volkova; Anatoliy Sachenko; Galina Shcherbakova; Jacek Woloszyn; Xinlin Liu; Viktor Krylov; Su Jun; Natalya Volkova; Anatoliy Sachenko; Galina Shcherbakova; Jacek Woloszyn

doi:10.3934/mbe.2022326

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 7: 6923-6939. doi: 10.3934/mbe.2022326

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Research article Special Issues

Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology

1.
School of Computer Science, Hubei University of Technology, Wuhan 430068, China
2.
Department of Applied Mathematics and Information Technologies, Odessa National Polytechnic University, Odessa 65044, Ukraine
3.
Department of Informatics and Teleinformatics, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom 26600, Poland
4.
Research Institute for Intelligent Computer Systems, West Ukrainian National University, Ternopil 46009, Ukraine
5.
Department of Information Systems, Odessa National Polytechnic University, Odessa 65044, Ukraine

Academic Editor: Ivan Izonin

Received: 30 January 2022 Revised: 16 April 2022 Accepted: 20 April 2022 Published: 09 May 2022

An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved vector-difference method for spectral-statistical texture segmentation has been developed. The method is based on the estimation of the number of features and subsequent calculation of a specific texture feature, and it uses wavelets obtained by transforming the graph of the power function at the stage of contour segmentation. Based on the above, the authors developed a modulus for spectral-statistical texture segmentation, which they applied to segment images of psoriatic disease; the Pratt's criterion was used to assess the quality of segmentation. The reliability of the classification of the spectral-statistical texture images was confirmed by using the True Positive Rate (TPR) and False Positive Rate (FPR) metrics calculated on the basis of the confusion matrix. The results of the experimental research confirmed the advantage of the proposed vector-difference method for the segmentation of spectral-statistical textures. The method enables further supplementation of the vector of features at the stage of identification through the use of the most informative features based on characteristic points for different degrees and types of psoriatic disease.
- identification,
- textural segmentation,
- spectral-statistical texture,
- vector-difference approach,
- wavelets,
- characteristic points
Citation: Xinlin Liu, Viktor Krylov, Su Jun, Natalya Volkova, Anatoliy Sachenko, Galina Shcherbakova, Jacek Woloszyn. Segmentation and identification of spectral and statistical textures for computer medical diagnostics in dermatology[J]. Mathematical Biosciences and Engineering, 2022, 19(7): 6923-6939. doi: 10.3934/mbe.2022326

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Abstract

An important component of the computer systems of medical diagnostics in dermatology is the device for recognition of visual images (DRVI), which includes identification and segmentation procedures to build the image of the object for recognition. In this study, the peculiarities of the application of detection, classification and vector-difference approaches for the segmentation of textures of different types in images of dermatological diseases were considered. To increase the quality of segmented images in dermatologic diagnostic systems using a DRVI, an improved vector-difference method for spectral-statistical texture segmentation has been developed. The method is based on the estimation of the number of features and subsequent calculation of a specific texture feature, and it uses wavelets obtained by transforming the graph of the power function at the stage of contour segmentation. Based on the above, the authors developed a modulus for spectral-statistical texture segmentation, which they applied to segment images of psoriatic disease; the Pratt's criterion was used to assess the quality of segmentation. The reliability of the classification of the spectral-statistical texture images was confirmed by using the True Positive Rate (TPR) and False Positive Rate (FPR) metrics calculated on the basis of the confusion matrix. The results of the experimental research confirmed the advantage of the proposed vector-difference method for the segmentation of spectral-statistical textures. The method enables further supplementation of the vector of features at the stage of identification through the use of the most informative features based on characteristic points for different degrees and types of psoriatic disease.

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