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A Hybrid Machine Learning Approach Using LBP Descriptor and PCA for Age-Related Macular Degeneration Classification in OCTA Images

Alfahaid, A; Morris, T; Cootes, T; Keane, PA; Khalid, H; Pontikos, N; Sergouniotis, P; (2020) A Hybrid Machine Learning Approach Using LBP Descriptor and PCA for Age-Related Macular Degeneration Classification in OCTA Images. In: Zheng, Y and Williams, BM and Chen, K, (eds.) Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. (pp. pp. 231-241). Springer: Cham, Switzerland. Green open access

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Abstract

We propose a novel hybrid machine learning approach for age-related macular degeneration (AMD) classification to support the automated analysis of images captured by optical coherence tomography angiography (OCTA). The algorithm uses a Rotation Invariant Uniform Local Binary Patterns (LBP) descriptor to capture local texture patterns associated with AMD and Principal Component Analysis (PCA) to decorrelate texture features. The analysis is performed on the entire image without targeting any particular area. The study focuses on four distinct groups, namely, healthy; neovascular AMD (an advanced stage of AMD associated with choroidal neovascularisation (CNV)); non-neovascular AMD (AMD without the presence of CNV) and secondary CNV (CNV due to retinal pathology other than AMD). Validation sets were created using a Stratified K-Folds Cross-Validation strategy for limiting the overfitting problem. The overall performance was estimated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC). The classification was conducted as a binary classification problem. The best performance achieved with the SVM classifier based on the AUC score for: (i) healthy vs neovascular AMD was 100 % , (ii) neovascular AMD vs non-neovascular AMD was 85 % ; (iii) CNV (neovascular AMD plus secondary CNV) vs non-neovascular AMD was 83 % .

Type: Proceedings paper
Title: A Hybrid Machine Learning Approach Using LBP Descriptor and PCA for Age-Related Macular Degeneration Classification in OCTA Images
Event: MIUA 2019: Annual Conference on Medical Image Understanding and Analysis
ISBN-13: 978-3-030-39342-7
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-39343-4_20
Publisher version: https://doi.org/10.1007/978-3-030-39343-4_20
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Optical coherence tomography angiography (OCTA), Age-related macular degeneration (AMD), Texture features
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10092047
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