Image classification : a study in age-related macular degeneration screening

Ahmad Hijazi, Mohd Hanafi
Image classification : a study in age-related macular degeneration screening. Doctor of Philosophy thesis, University of Liverpool.

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This thesis presents research work conducted in the field of image mining. More specifically, the work is directed at the employment of image classification techniques to classify images where features of interest are very difficult to distinguish. In this context, three distinct approaches to image classification are proposed. The first is founded on a time series based image representation, whereby each image is defined in terms of histograms that in turn are presented as "time series" curves. A Case Based Reasoning (CBR) mechanism, coupled with a Time Series Analysis (TSA) technique, is then applied to classify new "unseen" images. The second proposed approach uses statistical parameters that are extracted from the images either directly or indirectly. These parameters are then represented in a tabular form from which a classifier can be built on. The third is founded on a tree based representation, whereby a hierarchical decomposition technique is proposed. The images are successively decomposed into smaller segments until each segment describes a uniform set of features. The resulting tree structures allow for the application of weighted frequent sub-graph mining to identify feature vectors representing each image. A standard classifier generator is then applied to this feature vector representation to produce the desired classifier. The presented evaluation, applied to all three approaches, is directed at the classification of retinal colour fundus images; the aim is to screen for an eye condition known as Age-related Macular Degeneration (AMD). Of all the approaches considered in this thesis, the tree based representation coupled with weighted frequent sub-graph mining produced the best performance. The evaluation also indicated that a sound foundation has been established for future potential AMD screening programmes.

Item Type: Thesis (Doctor of Philosophy)
Additional Information: Date: 2012-07 (completed)
Uncontrolled Keywords: Image classification, image mining, image processing
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 10 Jan 2013 09:33
Last Modified: 16 Dec 2022 04:36
DOI: 10.17638/00007093