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Segmentation in microscopic images
T.Kovács, Matúš ; Jiřík, Radovan (referee) ; Kolář, Radim (advisor)
This thesis deals with the segmentation of microscopic images from plant sections. It describes the importance of the histogram for obtaining information from the image, and the utilization of the wavelet transformation for the preprocessing of the images. The thesis describes and categorizes different segmentation methods. In the thesis we use MATLAB for the validation of the presented theories and as the interface for creating a software model. The created software application automatically analyzes and evaluates microscopic images.
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Filtering methods for NMR measurements
Nezhyba, Jiří ; Mikulka, Jan (referee) ; Gescheidtová, Eva (advisor)
This master’s thesis deals with the wavelet transform and its use in processing and removing noise from images acquired by nuclear magnetic resonance. It defines fundamental terms for this work as mother wavelet or thresholding. Above all, it describes the principle of wavelet transform, thresholding techniques and criteria for evaluating the effectiveness of filtration. It describes the relation between wavelet transforms and digital filter banks. The experimental section describes the designed filtering method for removing noise from an image captured by the technique of nuclear magnetic resonance. We applied to different kinds of mother wavelets. Evaluation of the effectiveness of filtering was performed using the signal to noise ratio, relative contrast and the steepness of the intensity changes in signal intensity. It also discusses the comparison of properties of the image and selecting the mother wavelets based on image characteristics. Images were compared in terms of a histogram, cumulative histogram, k-space and the difference image.
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Enhancement of bio-medical image signals
Gregor, Michal ; Šmirg, Ondřej (referee) ; Přinosil, Jiří (advisor)
When scanning biomedical images by magnetic resonance or ultrasound, unwanted elements in the form of noise are entered to the image. With help of various methods it is possible the noise from the image partially remove. There are many methods for noise reduction and every one works on a different principle. As a result of this the results of these methods are different and is necessary for them to be objectively assessed. There is use for the adjustment of the images wavelet transformation and some treshold techniques in the work. The quality of the resulting pictures is tested by the methods for objective quallity tests. Testing was done in the MATLAB program environment on the pictures from magnetic resonance and pictures from ultrasound.
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Thresholding rules for noise reduction in sound signals
Ráček, Tomáš ; Schimmel, Jiří (referee) ; Balík, Miroslav (advisor)
The master's thesis focuses on the study of algorithms dealing with noise separation from musical signal. The first chapter is an introduction into methods which are used for noise removal of the musical signal. Furthermore, this chapter describes theory to the issue, specifically a description of transformations for converting from time to frequency domain, and finally thresholding method of spectral coefficients is explained in detail. The aim of the second chapter is an analysis of the proposed algorithm, which is engaged in testing. From the beginning fast algorithms of gradual transformation are described and then a detailed description of the algorithm as a whole. Later, this chapter deals with the selection of audio recordings and with preparation of these recordings for the actual testing. Finally, testing of audio samples is presented in the third chapter of this thesis. This chapter also concludes comparison of individual transformations, achieved results and review of algorithm.
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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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Monitoring Trends of Electrical Activity of the Heart Using Time-Frequency Decomposition
Čáp, Martin ; Kozumplík, Jiří (referee) ; Provazník, Ivo (advisor)
Work is aimed at the time-frequency decomposition of a signal application for monitoring the EKG trend progression. Goal is to create algorithm which would watch changes in the ST segment in EKG recording and its realization in the Matlab program. Analyzed is substance of the origin of EKG and its measuring. For trend calculations after reading the signal is necessary to preprocess the signal, it consists of filtration and detection of necessary points of EKG signal. For taking apart, also filtration and measuring the signal is used wavelet transformation. Source of the data is biomedicine database Physionet. As an outcome of the algorithm are drawn ST segment trends for three recordings from three different patients and its comparison with reference method of ST qualification. For qualification of the heart stability, as a system, where designed methods watching differences in position of the maximal value in two-zone spectrum and the Poincare mapping method. Realized method is attached to this thesis.
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Objects Classification in Images
Gabriel, Petr ; Petyovský, Petr (referee) ; Janáková, Ilona (advisor)
This master's thesis deal with problems of classification objects on the basis of atributes get from images. This thesis pertain to a branch of computer vision. Describe possible instruments of classification (e.g. neural networks, decision tree, etc.). Essential part is description objects by means of atributes. They are imputs to classifier. Practical part of this thesis deal with classification of object collection, which can be usually found at home (e.g. scissors, compact disc, sticky, etc.). Analyzed image is preprocessed , segmented by thresholding in HSV color map. Then defects caused by a segmentation are reconstructed by morfological operations. After are determined atribute values, which are imputs to classifier. Classifier has form of decision tree.
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Analysis of Colour Retinal Images Aimed at Segmentation of Vessel Structures
Odstrčilík, Jan ; Jiřík, Radovan (referee) ; Jan, Jiří (advisor)
Segmentation of vessel structure is an important phase in analysis of retinal images. The resulting vessel system description may be important for diagnostic of many eye and cardiovascular diseases. A method for automatic segmentation of the vessel structure in colour retinal images is presented in the thesis. The method utilises 2D matched filtering to detect presence of short linear vessel sections of a particular thickness and orientation. The approach correlates the local image areas with a 2D masks based on a typical brightness profile perpendicular to vessels of a particular width. Three different approximated profiles are used and corresponding matched filters are designed for: thin, medium and thick vessels. The evaluation of typical vessel profiles and filter design are described in chapter 3 and chapter 4. The parametric images obtained by convolution of the image with the masks are then thresholded in order to obtain binary representation of vessel structure. The three binary representations are consequently combined to provide the best available rough vessel map, which is finalised by complementing the obviously missing vessel sections and cleaning the disconnected fractional artefacts. The thresholding algorithm and final steps of processing are mentioned in chapter 5 and chapter 6. The method has been implemented by computer and the program for automatic vessel segmentation has been developed using database of real retinal images. The efficiency of the method has been finally evaluated on images from the standard database DRIVE.
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Segmentation in the color fundus imges
Malínský, Miloš ; Jiřík, Radovan (referee) ; Kolář, Radim (advisor)
Optic nerve head and macula are important structures in fundus images. Detection and measurement plays crucial role in several diagnosis methods of optic disease. This work is focused on the detection of the central point of macula and optic nerve head, where the inner border is detected too. There are many methods for extracting this structure in retinal images. Due to the unique properties of each acquisition technique, a single generally acknowledged detection algorithm does not exist. The whole detection process is described from preprocessing through segmentation towards postprocessing. Presented methods are based on the combination of correlation techniques, Hough transform, active contours and morphological operations. The detected contours of the optic nerve head are evaluated and quantitatively compared with the contour drawn by experienced ophthalmologist. The master thesis contains quantity of images that help to describe detection methods.
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Study population of bacteria by image analysis methods
Ševčík, Jan ; Veselá, Mária (referee) ; Zmeškal, Oldřich (advisor)
This bachelor thesis is focused on monitoring and application of image analysis methods on Bacillus megaterium and Cupriavidus necator bacteria. The introduction describes a general knowledge about bacteria, microscopic and image analysis methods. At first it was necessary to prepare suitable microscopic specimens using Gram staining for the observation of a bacteria cell. Every picture of bacteria was taken using a CCD camera and a microscope. Box–Counting method was used to identify the optimal threshold value, corresponding to their fractal measure (or surface areas covered with bacteria) and fractal dimension (or entropy of their arrangement on the surface area). After thresholding, the images were further processed using the Mass–Radius method, which defines a distribution of bacteria relative to the centroid of a cluster.
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