Alpha mean trim texture descriptors for optical coherence tomography eye classification

Alex Liew; Larry Ryan; Sos Agaian

doi:10.1117/12.2618059

27 May 2022 Alpha mean trim texture descriptors for optical coherence tomography eye classification

Alex Liew, Larry Ryan, Sos Agaian

Proceedings Volume 12100, Multimodal Image Exploitation and Learning 2022; 121000F (2022) https://doi.org/10.1117/12.2618059
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

Accurate ocular disorder classification and estimation of cornea depth and morphological changes depends on clear imaging of the affected structures. Ophthalmologists typically employ Optical Coherence Tomography (OCT) to help diagnose these conditions. This paper presents a new method called Alpha Mean Trim Local Binary Pattern (AMT-LPB) for automated texture classification of specific macular disease detected on OCT images of the retinal membrane. The performance of the proposed method achieved an overall accuracy of 99% using 10-fold cross-validation on the Duke University dataset [9].

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Alex Liew, Larry Ryan, and Sos Agaian "Alpha mean trim texture descriptors for optical coherence tomography eye classification", Proc. SPIE 12100, Multimodal Image Exploitation and Learning 2022, 121000F (27 May 2022); https://doi.org/10.1117/12.2618059

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