Volumetric image classification using homogeneous decomposition and dictionary learning: A study using retinal optical coherence tomography for detecting age-related macular degeneration

Albarrak, Abdulrahman, Coenen, Frans ORCID: 0000-0003-1026-6649 and Zheng, Yalin ORCID: 0000-0002-7873-0922 (2017) Volumetric image classification using homogeneous decomposition and dictionary learning: A study using retinal optical coherence tomography for detecting age-related macular degeneration. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 55. pp. 113-123.

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Abstract

Three-dimensional (3D) (volumetric) diagnostic imaging techniques are indispensable with respect to the diagnosis and management of many medical conditions. However there is a lack of automated diagnosis techniques to facilitate such 3D image analysis (although some support tools do exist). This paper proposes a novel framework for volumetric medical image classification founded on homogeneous decomposition and dictionary learning. In the proposed framework each image (volume) is recursively decomposed until homogeneous regions are arrived at. Each region is represented using a Histogram of Oriented Gradients (HOG) which is transformed into a set of feature vectors. The Gaussian Mixture Model (GMM) is then used to generate a "dictionary" and the Improved Fisher Kernel (IFK) approach is used to encode feature vectors so as to generate a single feature vector for each volume, which can then be fed into a classifier generator. The principal advantage offered by the framework is that it does not require the detection (segmentation) of specific objects within the input data. The nature of the framework is fully described. A wide range of experiments was conducted with which to analyse the operation of the proposed framework and these are also reported fully in the paper. Although the proposed approach is generally applicable to 3D volumetric images, the focus for the work is 3D retinal Optical Coherence Tomography (OCT) images in the context of the diagnosis of Age-related Macular Degeneration (AMD). The results indicate that excellent diagnostic predictions can be produced using the proposed framework.

Item Type:	Article
Uncontrolled Keywords:	Feature selection, Image classification, Dictionary learning, Homogeneous decomposition, Optical Coherence Tomography (OCT), Age-related Macular Degeneration (AMD)
Depositing User:	Symplectic Admin
Date Deposited:	25 Jan 2017 11:19
Last Modified:	12 Oct 2023 09:00
DOI:	10.1016/j.compmedimag.2016.07.007
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3003430