Multi-kernel Wiener local binary patterns for OCT ocular disease detections with resiliency to Gaussian noises

Alex Liew

doi:10.1117/12.3011423

7 June 2024 Multi-kernel Wiener local binary patterns for OCT ocular disease detections with resiliency to Gaussian noises

Alex Liew

Author Affiliations +

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 130330H (2024) https://doi.org/10.1117/12.3011423
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

Age-related macular degeneration (AMD) and Diabetic macular edema (DME) develop from irregularities in a section of the retina, causing vision impairment. Optical Coherence Tomography (OCT) imaging serves as the standard for identification, classification, and diagnosis of AMD and DME, determining locations of normal and irregular vascular patterns. However, challenges arise when OCT images are compromised by projection and motion artifacts concealing small lesions. This paper aims to develop an automated system for quantifying and categorizing AMD and DME (diabetic macula edema). The proposed approach, Multi-Kernel Wiener Local Binary Patterns (MKW-LBP) uses kernels of various sizes for feature extractions. Our proposed method is twofold: (1) Wiener patterns extract retinal features, robust against motion artifacts, thus preserving lesion visibility, and (2) multi-kernel vectorization exploits textural feature. Computer simulations demonstrate that the proposed technique achieves an overall accuracy of 98% through ten-fold cross-validation on the Duke University dataset. Furthermore, our system exhibits strong resistance against added Gaussian Noise, ensuring reliable performance under severe noise.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Alex Liew "Multi-kernel Wiener local binary patterns for OCT ocular disease detections with resiliency to Gaussian noises", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 130330H (7 June 2024); https://doi.org/10.1117/12.3011423

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