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Microaneurysms and hemorrhages detection in retinal images
Tobiášová, Nela ; Štohanzlová, Petra (referee) ; Kolář, Radim (advisor)
Diabetic retinopathy is a serious eye complication of diabetes mellitus and one of the major causes of blindness in the world. The microaneurysms and the haemorrhages are the pathologies of diabetic retinopathy. Their detection can halt or reverse the progression of this disease and prevent blindness. The algorithms could be helpful to ophthalmologists. This bacherol’s thesis is concerned with the detection of microaneurysms and haemorrhages in fundus images. The diabetic retinopathy, the types of lesions and the treatment methods are described in the first part of the paper. Existing methods are described as follows. The practical part of this work is aimed at the proposal and the detection of the red lesions. It consists of several steps, such as selecting the correct channel of RGB images, using local methods of contrast enhancement, edge detection, thresholding, creating a training set of the feature vector and the classification with the use of the neutral network.
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Wavelet Transform in Image Processing
Dostál, Martin ; Klíma, Ondřej (referee) ; Bařina, David (advisor)
The wavelet transform has been used for several decades and it is still an object of research - especially its recent modifications which are using the so-called second generation wavelets. It has several advantages over other integral transformations. The most important of them are the ability to localize both in time and frequency and an ability to decorrelate some real non-stationary signals such as images. For this reasons, the wavelet transform became an often used tool in many image processing tasks, for example in image compression, edge detection or contrast enhancement. In this thesis, the wavelet transform is explained, including the theoretical foundation and implementation for use with two-dimensional discrete signals. Some of the applications of the wavelet transform are presented and described. The wavelet transform showed to be suitable tool for edge detection, noise reduction, contrast enhancement and HDR compression.
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Microaneurysms and hemorrhages detection in retinal images
Tobiášová, Nela ; Štohanzlová, Petra (referee) ; Kolář, Radim (advisor)
Diabetic retinopathy is a serious eye complication of diabetes mellitus and one of the major causes of blindness in the world. The microaneurysms and the haemorrhages are the pathologies of diabetic retinopathy. Their detection can halt or reverse the progression of this disease and prevent blindness. The algorithms could be helpful to ophthalmologists. This bacherol’s thesis is concerned with the detection of microaneurysms and haemorrhages in fundus images. The diabetic retinopathy, the types of lesions and the treatment methods are described in the first part of the paper. Existing methods are described as follows. The practical part of this work is aimed at the proposal and the detection of the red lesions. It consists of several steps, such as selecting the correct channel of RGB images, using local methods of contrast enhancement, edge detection, thresholding, creating a training set of the feature vector and the classification with the use of the neutral network.
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Wavelet Transform in Image Processing
Dostál, Martin ; Klíma, Ondřej (referee) ; Bařina, David (advisor)
The wavelet transform has been used for several decades and it is still an object of research - especially its recent modifications which are using the so-called second generation wavelets. It has several advantages over other integral transformations. The most important of them are the ability to localize both in time and frequency and an ability to decorrelate some real non-stationary signals such as images. For this reasons, the wavelet transform became an often used tool in many image processing tasks, for example in image compression, edge detection or contrast enhancement. In this thesis, the wavelet transform is explained, including the theoretical foundation and implementation for use with two-dimensional discrete signals. Some of the applications of the wavelet transform are presented and described. The wavelet transform showed to be suitable tool for edge detection, noise reduction, contrast enhancement and HDR compression.
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