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Segmentation of arterial wall in high resolution retinal images
Polachová, Natálie ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
This thesis focuses on automatic segmentation of retinal arterial walls in images acquired using adaptive optics. Adaptive optics is a non-invasive imaging method that provides high lateral resolution and allows detailed observation of retinal microstructures, including arterial walls. This technology is crucial for early diagnosis of serious diseases such as arterial hypertension and diabetic retinopathy. The main objective of this work was to detect the arterial lumen and segment its walls. Morphological and filtration techniques were used for lumen detection. For arterial wall segmentation, brightness profiles along the detected lumen were analyzed and active contour and spline methods were used. The results show that the active contour segmentation method improves the accuracy of arterial wall detection, especially in high-contrast regions. This thesis summarizes the findings and proposes improvements in the detection of the inner side of the arterial wall, which reduces the segmentation success rate in this work.
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Segmentation of important structures in retinal images
Trojánek, Václav ; Mézl, Martin (referee) ; Odstrčilík, Jan (advisor)
This bachelor thesis focuses on the segmentation of significant structures in retinal image data to improve the diagnosis and treatment of ocular diseases. Methods of retinal image analysis are investigated and implemented in this thesis. The thesis begins with an overview of the anatomy of the eye and the principles of background eye imaging using a fundus camera and an experimental video ophthalmoscope. This is followed by a detailed literature search focusing on current methods for the detection and segmentation of diagnostically important structures such as the optic disc, macula and blood vessels. A key part of the work is the implementation and testing of selected algorithms, including Hough transform for optic disc detection and OTSU thresholding for blood vessel segmentation and yellow spot detection based on previous optic disc segmentation.
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Conversion of fingerprints captured by a mobile device into a standardized format - image editing
Mucha, Vojtěch ; Říha, Kamil (referee) ; Číka, Petr (advisor)
This bachelor thesis deals with the issue of fingerprint conversion taken by a mobile device into a standardized format. In the present day, mobile devices are used more and more often to acquire biometric data, fingerprints included. Processing and standardization of such data is an essential part of the subsequent biometric analysis. The aim of the work is to design and implement an algorithm which would convert a photo of a finger into a grey scale picture of its fingerprint with distinct papillary lines and subdued valleys. The algorithm is implemented in C++ using OpenCV library and a trained neural network for finger detection from hand image. The achieved results are evaluated according to the algorithms for assessing the quality of fingerprints NFIQ 2 and Innovatrics.
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Market segmentation using statistical methods
Bystřická, Michaela ; Marciánová, Pavla (referee) ; Schüller, David (advisor)
The thesis is focused on the segmentation of customers of selected summer swimming pool. The first part of the thesis is devoted to the theoretical concept of the chosen issue. In the analytical part, a summer swimming pool is presented and selected analyses are carried out. The analytical part also includes a questionnaire survey. In the last part of the thesis, measures are proposed that would lead to an increase in the level of services for the selected customer segment.
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Road and path segmentation in images for autonomous driving scenario
Janíček, Ondřej ; Cihlář, Miloš (referee) ; Svědiroh, Stanislav (advisor)
This bachelor's thesis deals with the topic of segmentation of roads and paths for the purposes of autonomous driving. In the theoretical part, it deals with computer vision, simple segmentation methods, and practical solutions to the problem using convolutional neural networks and classical methods. In the practical part, the work deals with the collection of test data, the selection of a suitable programming language, and the selection of suitable libraries. Subsequently, the procedure for programming our own solution will be presented. Here it starts with pre-processing to convert the image into a grayscale image and filtering the noise, then finding the edges in the image using the Canny edge detector, followed by the definition of the region of interest, with the subsequent Hough transform to detect the straight lines in the image, and in the last stage, filtering the horizontal lines and averaging the remaining lines. At the end of the thesis, the results of the presented solution are compared with respect to robustness and computational complexity.
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Ischemic thrombus analysis in multiphasic brain stroke CT data
Mikešová, Tereza ; Holeček, Tomáš (referee) ; Jakubíček, Roman (advisor)
This master thesis deals with analysis of ischemic thrombus in brain CT scans. In theoretical part, a review of methods, especially thrombus segmentation, is developed. Furthermore, the anatomy of cerebral arteries and acute ischemic stroke is summarized. Selected methods from the field of image processing are briefly described. The practical part results in a comparison of thrombus segmentation methods. The segmentation itself was preceded by data preprocessing, which is described in the theses, and the creation of a manual annotation database. The best implemented method was found to be the adaptive thresholding method, which achieved a Dice score of 0,4555. By combining the methods appropriately, a final Dice score of 0,5145 was achieved. Thrombus parameters were then calculated from the segmented volumes. The median intensity value was 51,55~HU, the median length was 15,16 mm, and the median volume was determined to be 65,34 mm3. Subsequent correlation analysis showed no significant relationship between the derived parameters.
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Methods for biomedical image signal segmentation
Krumpholc, Lukáš ; Šmirg, Ondřej (referee) ; Přinosil, Jiří (advisor)
This work deals with methods of segmentation of biomedical image signals. It describes, sums up and compares representative methods of digital image processing. Segmentation based on parametric representation is one of the mentioned methods. So as the basic parameter can be chosen for example luminance and the final binary image is obtained by thresholding. Next described method is segmentation based on edge representation. This method can be divided into edge detection by the help of edge detectors and of Hough transformation. Edge detectors work with the first and second derivation. The following method is region-based segmentation, which can be used for a image with noise. This category can be divided into three parts. The first one is segmentation via splitting and merging regions, when the image is split and the created regions are tested on a defined condition. If the condition is satisfied, the region merges and doesn’t continue splitting. The second one is region growing segmentation, when adjacent pixels with a similar intensity of luminance are grouped together and create a segmentated region. Third one is watershed segmentation algorithm based on the idea of water diffusion on uneven surface. The last group of methods is segmentation via flexible and active contours. Here is described an active shape model proceeding from a possibility to deform models so that they match with sample shapes. Next I also describe method Snakes, where occurs gradual contour shaping up to the edge of the object in the image. For the final editing is used mathematical morphology of segmentated images. I aimed to meet methods of image signals segmentation, to cover the chosen methods as a script in programming language Matlab and to check their properties on images.
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A convolutional neural network for image segmentation
Mitrenga, Michal ; Petyovský, Petr (referee) ; Jirsík, Václav (advisor)
The aim of the bachelor thesis is to learn more about the problem of convolutional neural networks and to realize image segmentation. This theme includes the field of computer vision, which is used in systems of artificial intelligence. Special Attention is paid to the image segmentation process. Furthermore, the thesis deals with the basic principles of artificial neural networks, the structure of convolutional neural networks and especially with the description of individual semantic segmentation architectures. The chosen SegNet architecture is used in a practical application along with a pre-learned network. Part of the work is a database of CamVid images, which is used for training. For testing, a custom image database is created. Practical part is focused on CNN training and searching for unsuitable parameters for network learning using SW Matlab.
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