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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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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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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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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.
|
|
|
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.
|
|
|
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.
|
|
|
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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