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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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Detection of face parts in the thermographic spectrum
Šujan, Miroslav ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
Master´s thesis deals with current problems of face detection and its parts in the infrared thermographic spectrum. Most previously published literature deals with the detection in the visible spectrum, making the thermographic detection range an interesting alternative. The work deals with the processing of image signals, images and faces in thermographic spectrum, selected methods of face detection and its parts and also deals with practical system design for detecting facial parts in this spectrum and its subsequent testing.
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Scene Analysis Based on the 2D Images
Hejtmánek, Martin ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
This thesis deals with an object surface analysis in a simple scene represented by two-dimensional raster image. It summarizes the most common methods used within this branch of information technology and explains both their advantages and drawbacks. It introduces the design of an surface profile analysis algorithm based on the lighting analysis using knowledge and experiences from previous work. It contains a detailed description of the implemented algorithm and discusses the experimental results. It also brings up options for the possible enhancement of the projected algorithm.
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Forensic Optical Fingerprint Comparator
Dvořák, Marek ; Klubal, Ondřej (referee) ; Drahanský, Martin (advisor)
My Bachelor Thesis deals with the design and creation of an application that replaces the function of forensic optical comparator. It also describes features of fingerprints and their prominent points, which are used for recognition of individuals. Work with fingerprints is very sensitive as for the quality of images. For this reason a Gabor filter has been implemented and described. Furthermore, the thesis describes basic functions used for work with images.
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Face detection in image
Koudela, Ondřej ; Harabiš, Vratislav (referee) ; Mézl, Martin (advisor)
This work is basic overview over methods of face detection in picture and it's components. The main part of the work is an example of algorithm developed in MATLAB programming language for face detection based on skin color segmentation in different color spaces and their implementation in real time. The basic algorithm is discussed in several steps with shown results for each of color spaces and in the end of the work is evaluation of used methods. This work also includes a simulation of processing real-time detection in MATLAB Simulink.
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Methodology of Real Estate Market Segmentation for the Valuation Process
Dadák, Michal ; Bradáč, Albert (referee) ; Cupal, Martin (advisor)
This master thesis is focused on the analysis of the real estate market and its segmentation. The beginning of the thesis deals with the basics of the real estate economy and consequently with the main segments on the real estate market. Different statistical and mathematical methods are used in the segmentation of the housing market. The thesis is closed by the analysis of the real estate market and the demonstration and description of how to separate the segment from the market. The output of the work is the recommended methodological procedure for the appraisers.
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Segmentation of ribs in thoracic CT scans
Kašík, Ondřej ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
This thesis deals with design and implementation of an algorithm for segmentation of ribs from thoracic CT data. For the segmentation method of rib centerlines detection is chosen. The first step of this approach is to extract the centerlines of all the bones located in the scan. These centerlines are divided into short primitives, which are subsequently classified into couple of categories, depending on whether they represent the centerline of the rib. Subsequently, the centrelines of ribs are used as the seed points of the region growing algorithm in three-dimensional space, which realizes the final segmentation of the ribs. Within the work, a database of 10 CT scans was manually annotated, which was subsequently used to validate a performance of the proposed segmentation approach. The achieved success rate of primitive classification is 96,7 %, the success rate of rib segmentation (Dice coefficient) is 86,8 %.
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