Local bit-plane neighbour dissimilarity pattern in non-subsampled shearlet transform domain for bio-medical image retrieval

Hilly Gohain Baruah; Vijay Kumar Nath; Deepika Hazarika; Rakcinpha Hatibaruah; Hilly Gohain Baruah; Vijay Kumar Nath; Deepika Hazarika; Rakcinpha Hatibaruah

doi:10.3934/mbe.2022075

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 2: 1609-1632. doi: 10.3934/mbe.2022075

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Research article

Local bit-plane neighbour dissimilarity pattern in non-subsampled shearlet transform domain for bio-medical image retrieval

Department of Electronics and Communication Engineering, School of Engineering, Tezpur University, Napaam, Tezpur, Assam 784028, India

Academic Editor: Weining Wu

Received: 10 September 2021 Accepted: 01 December 2021 Published: 13 December 2021

This paper introduces a novel descriptor non-subsampled shearlet transform (NSST) local bit-plane neighbour dissimilarity pattern (NSST-LBNDP) for biomedical image retrieval based on NSST, bit-plane slicing and local pattern based features. In NSST-LBNDP, the input image is first decomposed by NSST, followed by introduction of non-linearity on the NSST coefficients by computing local energy features. The local energy features are next normalized into 8-bit values. The multiscale NSST is used to provide translational invariance and has flexible directional sensitivity to catch more anisotropic information of an image. The normalised NSST subband features are next decomposed into bit-plane slices in order to capture very fine to coarse subband details. Then each bit-plane slices of all the subbands are encoded by exploiting the dissimilarity relationship between each neighbouring pixel and its adjacent neighbours. Experiments on two computed tomography (CT) and one magnetic resonance imaging (MRI) image datasets confirms the superior results of NSST-LBNDP when compared to many recent well known relevant descriptors both in terms of average retrieval precision (ARP) and average retrieval recall (ARR).
- content based image retrieval,
- non subsampled shearlet transform (NSST),
- bit-plane decomposition,
- local patterns,
- features,
- dissimilarity,
- computed tomography (CT),
- magnetic resonance imaging (MRI)
Citation: Hilly Gohain Baruah, Vijay Kumar Nath, Deepika Hazarika, Rakcinpha Hatibaruah. Local bit-plane neighbour dissimilarity pattern in non-subsampled shearlet transform domain for bio-medical image retrieval[J]. Mathematical Biosciences and Engineering, 2022, 19(2): 1609-1632. doi: 10.3934/mbe.2022075

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Abstract

This paper introduces a novel descriptor non-subsampled shearlet transform (NSST) local bit-plane neighbour dissimilarity pattern (NSST-LBNDP) for biomedical image retrieval based on NSST, bit-plane slicing and local pattern based features. In NSST-LBNDP, the input image is first decomposed by NSST, followed by introduction of non-linearity on the NSST coefficients by computing local energy features. The local energy features are next normalized into 8-bit values. The multiscale NSST is used to provide translational invariance and has flexible directional sensitivity to catch more anisotropic information of an image. The normalised NSST subband features are next decomposed into bit-plane slices in order to capture very fine to coarse subband details. Then each bit-plane slices of all the subbands are encoded by exploiting the dissimilarity relationship between each neighbouring pixel and its adjacent neighbours. Experiments on two computed tomography (CT) and one magnetic resonance imaging (MRI) image datasets confirms the superior results of NSST-LBNDP when compared to many recent well known relevant descriptors both in terms of average retrieval precision (ARP) and average retrieval recall (ARR).

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