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Texture Analysis of Ophthalmologic Images
Kaňka, Jan ; Odstrčilík, Jan (referee) ; Jan, Jiří (advisor)
This thesis is concerned with creating of software for textural analysis of retinal images by statistic method called co-occurrence matrices as possible alternative method to detection of lesion of retina by the glaucoma diseases. The Glaucoma diseases without well-timed diagnostics and consequential treatment leads to blindness. The retinal images captured by fundus camera are common, easily processable and modern devices are able to make images of 10 Mpix resolution and bigger, what specifies outputs of analytic software. Method of co-occurrence analysis is simple and effective statistic. But computing demandingness, showing up with growing objective quality, comming up as disadvantage.
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Texture Analysis of Ophthalmologic Images
Kaňka, Jan ; Odstrčilík, Jan (referee) ; Jan, Jiří (advisor)
This thesis is concerned with creating of software for textural analysis of retinal images by statistic method called co-occurrence matrices as possible alternative method to detection of lesion of retina by the glaucoma diseases. The Glaucoma diseases without well-timed diagnostics and consequential treatment leads to blindness. The retinal images captured by fundus camera are common, easily processable and modern devices are able to make images of 10 Mpix resolution and bigger, what specifies outputs of analytic software. Method of co-occurrence analysis is simple and effective statistic. But computing demandingness, showing up with growing objective quality, comming up as disadvantage.
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Segmentation of 3D image data using advanced textural and shape features
Novosadová, Michaela ; PhD, Miloš Malínský, (referee) ; Jan, Jiří (advisor)
This thesis first describes theory of range of methods of textural and shape analysis. In several published articles some of the mentioned methods are used for automatic detection of lesion in spine in CT images. Some of these articles are shortly presented (in this thesis). Next part of the thesis includes description of various classifiers which are used for classification of feature vectors. Practical part of the thesis is a design and implementation of image data segmentation solution (metastatic lesions in vertebrae) with use of classification of feature vectors formed by texture and shape symptoms. The thesis also deals with the selection of significant features for segmentation. Segmentation algorithm is tested on medical data.
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