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Blood vessel segmentation in fundus images using classification methods
Šťastný, Pavel ; Štohanzlová, Petra (referee) ; Odstrčilík, Jan (advisor)
Segmentation blood vessel the local images of retinal is very important for evaluation and for diagnostics eye’s disease, especially diabetic retinopathy and glaucoma. This bachelor’s thesis is deal with segmentation blood vessel by classification methods. I used simple neural network as a classifier. First of all I taught her by delta rule and then I used matched filtering on the prepare image. At the end I compared all information with gold standard. Average va-lues from score for healthy images were sensitivity 0,7717, specificity 0,9571 and accuracy score 0,9225.
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Optic disc detection in retinal images
Jalůvková, Lenka ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
The bachelor thesis is focused on a detection of optic disc in the retinal images in order to propose and compare several existing methods. The detection is implemented as the Gaussian filter, matched filter and is done by vascular structure information. The DIARETDB1 database is used for testing. The best results have been achieved using Gaussian filter and detection by vascular structure information with success rate 81%. The description and comparison of all the algorithms can be found in this thesis.
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Segmentation in microscopic images
Vlk, Jaroslav ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
Work studying the properties of microscopic images and then the following applies in the segmentation of the image. Work is trying to use a simpler methods image processing mainly. At the same time, deals with methods for preprocessing image. In the choice of methods and the use of emphasis on speed and simplicity of calculation.
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Analysis of Colour Retinal Images Aimed at Segmentation of Vessel Structures
Odstrčilík, Jan ; Jiřík, Radovan (referee) ; Jan, Jiří (advisor)
Segmentation of vessel structure is an important phase in analysis of retinal images. The resulting vessel system description may be important for diagnostic of many eye and cardiovascular diseases. A method for automatic segmentation of the vessel structure in colour retinal images is presented in the thesis. The method utilises 2D matched filtering to detect presence of short linear vessel sections of a particular thickness and orientation. The approach correlates the local image areas with a 2D masks based on a typical brightness profile perpendicular to vessels of a particular width. Three different approximated profiles are used and corresponding matched filters are designed for: thin, medium and thick vessels. The evaluation of typical vessel profiles and filter design are described in chapter 3 and chapter 4. The parametric images obtained by convolution of the image with the masks are then thresholded in order to obtain binary representation of vessel structure. The three binary representations are consequently combined to provide the best available rough vessel map, which is finalised by complementing the obviously missing vessel sections and cleaning the disconnected fractional artefacts. The thresholding algorithm and final steps of processing are mentioned in chapter 5 and chapter 6. The method has been implemented by computer and the program for automatic vessel segmentation has been developed using database of real retinal images. The efficiency of the method has been finally evaluated on images from the standard database DRIVE.
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