National Repository of Grey Literature 7 records found  Search took 0.01 seconds. 
Robust Cell Nuclei Tracking Using Gaussian Mixture Shape Model
Vičar, Tomáš
The life cell microscopic imaging is a standard approach for studying of cancer cell morphology and behaviour during some treatment. In the dense cell cultures, tracking each cell nucleus is challenging task due to cell overlap and interactions. Moreover, for time-lapse sequences (lasting typically 20-30 hours) the robust automatic cell tracking is needed. This paper describes new method for fluorescence nuclei tracking based on Gaussian mixture model (GMM), and additionally, GMM modification allowing application to the images is also introduced. Method is mainly designed for robustness - tracking the highest possible number of nuclei in the whole sequence. Proposed algorithm proved to by very reliable with 80% of correctly tracked nuclei.
Active Contours Initialization for Cell Tracking in the Images from Holographic Microscope
Vičar, Tomáš
This paper describes the implementation of the segmentation method applied on images from holographic microscope. The method combines tresholding and active contour model. This segmentation method can be used on the first image of image sequence for creating contour initialization. Initialized contours, then can be used for cell tracking with active contours.
Features for the analysis and classification of cells in holographic microscope images
Navrátilová, Markéta ; Kolář, Radim (referee) ; Vičar, Tomáš (advisor)
This thesis deals with features used for analysis and classification of cell images captured by holographic microscope. Distinctive features are described together with tools for their classification. Features are extracted on provided segmented cells with use of Matlab programming environment. Based on extracted features the cells are classified by SVM classificator. With use of clustering methods and dimensionality reduction different cell types are analyzed. Reliabity of each feature is tested.
Automatic classification of pronunciation of the letter „R“
Hrušovský, Enrik ; Vičar, Tomáš (referee) ; Harabiš, Vratislav (advisor)
This diploma thesis deals with automatic clasification of vowel R. Purpose of this thesis is to made program for detection of pronounciation of speech defects at vowel R in children. In thesis are processed parts as speech creation, speech therapy, dyslalia and subsequently speech signal processing and analysis methods. In the last part is designed software for automatic detection of pronounciation of vowel R. For recognition of pronounciation is used algorithm MFCC for extracting features. This features are subsequently classified by neural network to the group of correct or incorrect pronounciation and is evaluated classification success.
Blood vessel segmentation in retinal images using deep learning approaches
Serečunová, Stanislava ; Vičar, Tomáš (referee) ; Kolář, Radim (advisor)
This diploma thesis deals with the application of deep neural networks with focus on image segmentation. The theoretical part contains a description of deep neural networks and a summary of widely used convolutional architectures for segmentation of objects from the image. Practical part of the work was devoted to testing of an existing network architectures. For this purpose, an open-source software library Tensorflow, implemented in Python programming language, was used. A frequent problem incorporating the use of convolutional neural networks is the requirement on large amount of input data. In order to overcome this obstacle a new data set, consisting of a combination of five freely available databases was created. The selected U-net network architecture was tested by first modification of the newly created data set. Based on the test results, the chosen network architecture has been modified. By these means a new network has been created achieving better performance in comparison to the original network. The modified architecture is then trained on a newly created data set, that contains images of different types taken with various fundus cameras. As a result, the trained network is more robust and allows segmentation of retina blood vessels from images with different parameters. The modified architecture was tested on the STARE, CHASE, and HRF databases. Results were compared with published segmentation methods from literature, which are based on convolutional neural networks, as well as classical segmentation methods. The created network shows a high success rate of retina blood vessels segmentation comparable to state-of-the-art methods.
Module for Electrodermal Activity recording
Vičar, Tomáš ; Harabiš, Vratislav (referee) ; Bubník, Karel (advisor)
This thesis describes electrodermal activity (EDA) and its origin based on the properties of the skin and thermoregulation of body. EDA is a signal having a close relationship to psychophysiology and its help we can evaluate a variety of emotional, motoric and attentional effects on the human organism. The thesis also discusses the possibility of sensing skin potential and conductatce and how to construct a module for its scanning and uploading to computer.
Cell tracking in images from holographic microscope
Vičar, Tomáš ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
This thesis focuses on cell tracking in image sequences acquired using a multimodal holographic microscope (MHM). The principles of holographic microscopy are described together with the application in cells acquisition. The main part of the thesis describes a complete approach for segmentation and tracking of single cells in acquired in long-term sequences. The approach is designed based on parametric active contour models with specific modifications to achieve reasonable precision and robustness. The implemented method is described in detail, including the evaluation and demonstration of results.

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