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Registration of image sequences from experimental video-ophthalmoscope
Bjelová, Martina ; Vičar, Tomáš (oponent) ; Kolář, Radim (vedoucí práce)
The topic of this thesis is registration of image sequences captured by experimental ophthalmoscope. It contains anatomical description of the visual system as well as the description of functions of selected ophthalmoscopic devices. The next covered topic is theoretical summary of registration process, which is followed by an overview of the used methods, which forms the basis of the design and implementation of the registration algorithm in the Python programming language. After implementation, the accuracy and computational complexity of a registration was evaluated. Tests of optimalization of the proposed approach were performed with regards to the obtained results, through which sufficiently accurate registration has been achieved, evaluated on the basis of Euclidean distances, standard deviation and visual observation. In case of high-quality recorded sequences, values of Euclidean distances ranged from 0.60 to 4.07 pixels on the contrary, values higher than 20 pixels occurred in the case of poor-quality recordings. Standard deviation values in recordings with high enough resolution have not reached worse results than 4.12.
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K-complex detection in sleep EEG
Bjelová, Martina ; Mézl, Martin (oponent) ; Králík, Martin (vedoucí práce)
This paper addresses detecting of K-complexes in sleeping EEG records. Polysomnography is the method, which is used for diagnostic and following therapy of many sleep disorders. For identifnging of sleep stages it is fundamental to know graphoelements, in which they are situate. K-complex is important indicator of second sleep stange and hence is essencial to know to detect this pattern. In this paper we focus on design and implementation of more algorithms for detection of these patterns with various characteristics. Among the proposed methods, the wavelet transform method was best evaluated. Performance of this detection reached values the average senzitivity 63,83 % and average positive predictive value 44,07 %.
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Registration of image sequences from experimental video-ophthalmoscope
Bjelová, Martina ; Vičar, Tomáš (oponent) ; Kolář, Radim (vedoucí práce)
The topic of this thesis is registration of image sequences captured by experimental ophthalmoscope. It contains anatomical description of the visual system as well as the description of functions of selected ophthalmoscopic devices. The next covered topic is theoretical summary of registration process, which is followed by an overview of the used methods, which forms the basis of the design and implementation of the registration algorithm in the Python programming language. After implementation, the accuracy and computational complexity of a registration was evaluated. Tests of optimalization of the proposed approach were performed with regards to the obtained results, through which sufficiently accurate registration has been achieved, evaluated on the basis of Euclidean distances, standard deviation and visual observation. In case of high-quality recorded sequences, values of Euclidean distances ranged from 0.60 to 4.07 pixels on the contrary, values higher than 20 pixels occurred in the case of poor-quality recordings. Standard deviation values in recordings with high enough resolution have not reached worse results than 4.12.
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K-complex detection in sleep EEG
Bjelová, Martina ; Mézl, Martin (oponent) ; Králík, Martin (vedoucí práce)
This paper addresses detecting of K-complexes in sleeping EEG records. Polysomnography is the method, which is used for diagnostic and following therapy of many sleep disorders. For identifnging of sleep stages it is fundamental to know graphoelements, in which they are situate. K-complex is important indicator of second sleep stange and hence is essencial to know to detect this pattern. In this paper we focus on design and implementation of more algorithms for detection of these patterns with various characteristics. Among the proposed methods, the wavelet transform method was best evaluated. Performance of this detection reached values the average senzitivity 63,83 % and average positive predictive value 44,07 %.
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