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AMD classification in choroidal OCT using hierarchical texton mining

Ravenscroft, Dafydd, Deng, Jingjing, Xie, Xianghua, Terry, Louise, Margrain, Tom H., North, Rachel V. and Wood, Ashley 2017. AMD classification in choroidal OCT using hierarchical texton mining. Lecture Notes in Artificial Intelligence 10617 , pp. 237-248. 10.1007/978-3-319-70353-4_21

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Abstract

In this paper, we propose a multi-step textural feature extraction and classification method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, extracts spatial information using clustering and Local Binary Patterns (LBP) and then generalizes the discriminative power by forming a histogram based descriptor. It integrates the concept of hierarchical texton mining and data driven kernel learning into a uniform framework. The proposed method is applied to a practical medical diagnosis problem of classifying different stages of Age-Related Macular Degeneration (AMD) using a dataset comprising long-wavelength Optical Coherence Tomography (OCT) images of the choroid. The results demonstrate the feasibility of our method for classifying different AMD stages using the textural information of the choroidal region.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Optometry and Vision Sciences
Publisher: Springer
ISSN: 0302-9743
Date of First Compliant Deposit: 23 January 2019
Last Modified: 17 Oct 2019 23:59
URI: http://orca.cf.ac.uk/id/eprint/118434

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