Ravenscroft, Dafydd, Deng, Jingjing, Xie, Xianghua, Terry, Louise  ORCID: https://orcid.org/0000-0002-6200-8230, Margrain, Tom H. ORCID: https://orcid.org/0000-0003-1280-0809, North, Rachel V. ORCID: https://orcid.org/0000-0002-6657-5099 and Wood, Ashley ORCID: https://orcid.org/0000-0002-9312-6184
2017.
AMD classification in choroidal OCT using hierarchical texton mining.
Presented at: ACIVS 2017,
Antwerp, Belgium,
18-21 Sep 2017.
Published in: Blanc-Talon, Jacques, Penne, Rudi, Philips, Wilfried, Popescu, Dan and Scheunders, Paul eds.
Advanced Concepts for Intelligent Vision Systems.
Lecture Notes in Computer Science.
Lecture Notes in Artificial Intelligence
, vol.10617
Cham, Switzerland:
Springer,
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: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Optometry and Vision Sciences |
| Publisher: | Springer |
| ISBN: | 978-3-319-70352-7 |
| ISSN: | 0302-9743 |
| Date of First Compliant Deposit: | 23 January 2019 |
| Last Modified: | 04 Dec 2024 18:00 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/118434 |
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