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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6456

Title: Structure analysis and lesion detection from retinal fundus images
Authors: Salazar Gonzalez, Ana
Advisors: Liu, X
Li, Y
Keywords: Image processing
Retinal analysis
Image segmentation
Medical images analysis
Retinal lesion detection
Publication Date: 2011
Publisher: Brunel University, School of Information Systems, Computing and Mathematics
Abstract: Ocular pathology is one of the main health problems worldwide. The number of people with retinopathy symptoms has increased considerably in recent years. Early adequate treatment has demonstrated to be effective to avoid the loss of the vision. The analysis of fundus images is a non intrusive option for periodical retinal screening. Different models designed for the analysis of retinal images are based on supervised methods, which require of hand labelled images and processing time as part of the training stage. On the other hand most of the methods have been designed under the basis of specific characteristics of the retinal images (e.g. field of view, resolution). This compromises its performance to a reduce group of retinal image with similar features. For these reasons an unsupervised model for the analysis of retinal image is required, a model that can work without human supervision or interaction. And that is able to perform on retinal images with different characteristics. In this research, we have worked on the development of this type of model. The system locates the eye structures (e.g. optic disc and blood vessels) as first step. Later, these structures are masked out from the retinal image in order to create a clear field to perform the lesion detection. We have selected the Graph Cut technique as a base to design the retinal structures segmentation methods. This selection allows incorporating prior knowledge to constraint the searching for the optimal segmentation. Different link weight assignments were formulated in order to attend the specific needs of the retinal structures (e.g. shape). This research project has put to work together the fields of image processing and ophthalmology to create a novel system that contribute significantly to the state of the art in medical image analysis. This new knowledge provides a new alternative to address the analysis of medical images and opens a new panorama for researchers exploring this research area.
Description: This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.
Sponsorship: Mexican National Council of Science and Technology
URI: http://bura.brunel.ac.uk/handle/2438/6456
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Theses

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