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Analysis of fundus images aimed to localize pathological areas
Hartlová, Marie ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
Diabetic retinopathy is a serious eye complication of diabetes mellitus and one of the major causes of blindness in the world. This thesis deals with detection of neovascularizations, which is the first manifestation of diabetic retinopathy in the retina. In summary, in this thesis describe the properties image data from digital fundus camera, image segmentation methods, methods for automatic blood vessels segmentation and detection of neovaskularizations. This information are used to create own method to detect neovaskularization.
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Analysis of retinal vessel pulsation
Holásková, Anna ; Mézl, Martin (referee) ; Odstrčilík, Jan (advisor)
The content of this work is the analysis of retinal vessels pulsation of video sequences acquired by experimental fundus camera based on measuring the brightness profile of the vessel. The first level of analysis in this work is the segmentation of blood vessels and diameter measurement of blood vessels during the sequence. The work contains research methods dealing with the diameter measurement and evaluation of pulsation and analysis segmentation methods using for analysing the vasculature of the retina. From these methods, a vessel tracking method was selected. In segmented video sequences is on the ground of intensity profiles analysed vessel pulsation. Analysis is also made on original dataset and results are discussed considering the frequency characteristic.
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Segmentation of choroid blood vessels in 3D images from optical coherence tomography
Maňkoš, Richard ; Kolář, Radim (referee) ; Štohanzlová, Petra (advisor)
This project deals with segmentation of choroid blood vessels in 3D images from optical coherence tomography. The first part is dedicated to theoretical description of the human eye. The second part describes basic information about the optical coherence tomography. In the third part two techniques, which are used on the blood vessels segmentation are described. The last part is practical and deals with an image processing in software OCTSEG and with own created algorithm for blood vessel segmentation.
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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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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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Blood vessel segmentation and modeling in fundus images
Václavík, Jan ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
Studies of the vascular tree in the retina have applications not only in the medical field but also biometrics. The mathematical description of the retinal vasculature could help facilitate and improve the diagnosis of certain diseases, their automatic localization or to accelerate the identification and verification of individuals. The aim is to design and develop an algorithm that will automatically approximate major retinal vessels by parabolic, linear and kvartic functions. The main part of this thesis is therefore devoted to this issue, including vascular segmentation using Gabor filters, morphological erosion, thresholding, skeletonization and the resulting optimization of the approximation model. The quality of the produced algorithm is discussed in the summary.
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Retinal images in biometry
Bujnošková, Eva ; Drahanský, Martin (referee) ; Kolář, Radim (advisor)
Retinal recognition is very efficient and almost non-fallible tool for persons' identification, thanks its advantages it can be used in cases when high security is needed. Process of the identification comes from successful vessel extraction and the transfer to binary image. After that this is used to look for the vessel bifurcations with help of skeletonization which is one of the operations of mathematical morphology. The parameter of the detection of bifurcations isn't enough therefore there are other information completed - thickness and the direction of vessel in the surroundings of known crossing. The best correlation between the parameters and the images in database is searched, than alignment is made, and with the certain probability the closest image is chosen to be proclaimed as the match. The solution uses also the second method to image processing - the method using image translation and evaluation of minimal distances between found bifurcations.
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Evaluation of Automatic Vessel Tree Segmentation Algorithms
Svoboda, Ondřej ; Jan, Jiří (referee) ; Odstrčilík, Jan (advisor)
Segmentation of the vasculature is an important step in the process of the retinal image analysis. The results of the analysis can be used to diagnose several eye and cardiovascular diseases. This work deals with the creation of gold standard database of high resolution retinal images and their use in evaluating the success of vascular automatic segmentation methods. The aim is to create the application, which will online evaluate the success of the automatic vessel segmentation methods. In brief, this work describes the characteristics of image data from digital fundus camera, the method of image segmentation and automatic segmentation methods of blood vessels. Furthermore, this work describes the gold standard, the databases of gold standards and ultimately the properties of the new database and the reason for HRF (High Resolution Fundus Images). The last chapter deals with methods of evaluating the success of vascular automatic detection methods and application development for this assessment.
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Blood vessel segmentation in fundus images
Šťastný, Pavel ; Mézl, Martin (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 gabor filtrer as a classifier. First was created filter bank and then using the convolution applicator pan the image. At the end I compared all information with gold standard. Average values from score for healthy images were sensitivity 0,8340, specificity 0,8709 and accuracy score 0,8663.
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