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Preprocessing of retinal images aimed at support diagnosis of glaucoma
Holásková, Anna ; Walek, Petr (referee) ; Odstrčilík, Jan (advisor)
Preprocessing of retinal images can serve as a first phase of the further image analysis or the first step preceding diagnosing of various eye diseases. The preprocessing thus represents methods of image adjustments that can improve visual characteristics of fundus images. These methods mainly include the removal of noise generated during data acquisition, contrast and brightness transformations, edge detection and thresholding. This work handles with the basic methods of image preprocessing and specific methods of preprocessing of retinal images. The preprocessing includes global illumination correction, high-pass and homomorphic filtering and adaptive enhancement of the images. Manual methods for fundus image preprocessing that are usually based on the doctor's experience can be used as well. Hence, a procedure for enhancement of retinal images using Adobe Photoshop is mentioned in this work too. Three methods for preprocessing of fundus images were selected and implemented in MATLAB programming software. These methods include homomorphic filtering, CLAHE (Contrast Limited Adaptive Histogram Equalization) and adaptive enhancement. Experimental program functions were created and tested on the available image data. Results of the selected methods are mentioned in the conclusion section. Instructions for use of implemented functions are in appendix.
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Fingerprint scanning
Kubiš, Michal ; Dobeš,, Michal (referee) ; Fedra, Petr (advisor)
Fingerprints are the oldest and most used form of biometric identification. A critical step is reliable extract minutiae from the fingerprint images. However fingerprint images are rarely of perfect quality, they may be degraded and corrupted due to natural variations in skin and sensing conditions. Thus, image enhancement techniques are necessary prior to minutiae extraction. This work includes implementation of three techniques for fingerprint image enhancement, minutiae extraction and consturction of fingerprint reading device. Experiments are realized with two sets of fingerprints to evaluate the performance of implemented techniques.
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Automatic detection of microcalcifications in mammogram images
Hývlová, Denisa ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
This bachelor thesis is focused on detection of microcalcification in mammography images. The introduction describes connection between their presence and breast cancer, principle of mammography and the DICOM standard used in radiology. In the following part the methods used for microcalcification enhancement and segmentation are explained. Detection algorithm based on wavelet transform, morphological closing and thresholding was designed in MATLAB. For evaluation of the results a graphical user interface was developed and an algorithm for automatic evaluation of the success rate in annotated mammography database was implemented.
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Detection and Quality Improvement of Face Objects in Low-Quality Source Images
Šoltis, Richard ; Tinka, Jan (referee) ; Drahanský, Martin (advisor)
The aim of this thesis was to construct an algorithm for the detection of human face from poor quality source images and subsequently improving the image of human face. The result of the work is an application with a graphical interface which detects human face objects from the input images and then improves these inherited faces from the point of quality and size. When creating the application, current techniques and algorithms such as neuron networks were used. They formed the basis for detection and image improvement, S3FD detection and last but not least the GAN network to improve the image. Part of the thesis is testing the individual parts of the application in predefined scenarios as well as testing a comprehensive run application.
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Image recognition for robotic hand
Labudová, Kristýna ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
This thesis concerns with processing of embedded terminals’ images and their classification. There is problematics of moire noise reduction thought filtration in frequency domain and the image normalization for further processing analyzed. Keypoints detectors and descriptors are used for image classification. Detectors FAST and Harris corner detector and descriptors SURF, BRIEF and BRISK are emphasized as well as their evaluation in terms of potential contribution to this work.
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Image Super-Resolution Using Deep Learning
Bublavý, Martin ; Juránková, Markéta (referee) ; Španěl, Michal (advisor)
The ability to identify and treat a variety of medical diseases is made possible by medical imaging, which is an essential component of contemporary healthcare. Yet, elements like noise and low resolution can have a negative impact on the quality of medical photographs. In this thesis, how to enhance the resolution and quality of medical images was investigated using MedSRGAN, a deep learning model built on generative adversarial networks (GANs). MedSRGAN was implemented and then applied to computed tomography (CT), one of the most utilized medical imaging methods.
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Detection and Quality Improvement of Face Objects in Low-Quality Source Images
Šoltis, Richard ; Tinka, Jan (referee) ; Drahanský, Martin (advisor)
The aim of this thesis was to construct an algorithm for the detection of human face from poor quality source images and subsequently improving the image of human face. The result of the work is an application with a graphical interface which detects human face objects from the input images and then improves these inherited faces from the point of quality and size. When creating the application, current techniques and algorithms such as neuron networks were used. They formed the basis for detection and image improvement, S3FD detection and last but not least the GAN network to improve the image. Part of the thesis is testing the individual parts of the application in predefined scenarios as well as testing a comprehensive run application.
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Automatic detection of microcalcifications in mammogram images
Hývlová, Denisa ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
This bachelor thesis is focused on detection of microcalcification in mammography images. The introduction describes connection between their presence and breast cancer, principle of mammography and the DICOM standard used in radiology. In the following part the methods used for microcalcification enhancement and segmentation are explained. Detection algorithm based on wavelet transform, morphological closing and thresholding was designed in MATLAB. For evaluation of the results a graphical user interface was developed and an algorithm for automatic evaluation of the success rate in annotated mammography database was implemented.
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Texture modeling applied to medical images
Remeš, Václav ; Haindl, Michal (advisor)
and contributions This thesis presents novel descriptive multidimensional Markovian textural models applied to computer aided diagnosis in the field of X-ray mammogra- phy. These general mathematical models, applicable in wide areas of texture modeling outside X-ray mammography as well, provide ideal visual verification using synthesis of the corresponding measured data spaces, contrary to stan- dard discriminative models. All achieved results in the thesis are extensively benchmarked. The thesis presents two methods for breast density classification in X-ray mammography. The methods were tested on the widely known MIAS database and the state-of-the art INbreast database, with competitive results. Several methods for completely automatic mammogram texture enhance- ment are presented. These methods are based on the descriptive textural mod- els developed in the thesis which automatically adapt to the analyzed X-ray texture, thus being universal for any type of input without the need of further manual tuning of specific parameters. The methods' outputs highlight regions of interest, detected as textural abnormalities. The methods provide the pos- sibility of enhancement tuned to specific types of mammogram tissue. Hence, the enhanced mammograms can help radiologists to decrease their false negative...
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Texture modeling applied to medical images
Remeš, Václav ; Haindl, Michal (advisor)
and contributions This thesis presents novel descriptive multidimensional Markovian textural models applied to computer aided diagnosis in the field of X-ray mammogra- phy. These general mathematical models, applicable in wide areas of texture modeling outside X-ray mammography as well, provide ideal visual verification using synthesis of the corresponding measured data spaces, contrary to stan- dard discriminative models. All achieved results in the thesis are extensively benchmarked. The thesis presents two methods for breast density classification in X-ray mammography. The methods were tested on the widely known MIAS database and the state-of-the art INbreast database, with competitive results. Several methods for completely automatic mammogram texture enhance- ment are presented. These methods are based on the descriptive textural mod- els developed in the thesis which automatically adapt to the analyzed X-ray texture, thus being universal for any type of input without the need of further manual tuning of specific parameters. The methods' outputs highlight regions of interest, detected as textural abnormalities. The methods provide the pos- sibility of enhancement tuned to specific types of mammogram tissue. Hence, the enhanced mammograms can help radiologists to decrease their false negative...
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