National Repository of Grey Literature 18 records found  1 - 10next  jump to record: Search took 0.00 seconds. 
Cell Detection Methods For The Images From Holographic Microscope
Vičar, Tomáš
Microscopical cell image analysis is widely used for cell behavior and morphology study. In dense cell cultures precise detection (separation) of a single cell is challenging task and it is important step for automatic cell analysis methods. There are a variety of methods, but most of them are less accurate for non-circular cells. This paper describes the common approaches for cell detection applied on images from holographic microscope. Linear discriminant analysis is used for combining results of these methods to obtain new more precise and robust approach.
Cell segmentation by pixel classification in images from various microscopic modalities
Vývoda, Jan ; Jakubíček, Roman (referee) ; Vičar, Tomáš (advisor)
This Bachelor thesis deals with cell segmentation by pixel classification of various microscopic modalities. There is a summary of possible features and also some of the classifier suitable for this kind of segmentation are mentioned here. In the practical part of the thesis, there are results for chosen features and classifier.
Detection of pathologies in retinal images
Mesíková, Klaudia ; Kolář, Radim (referee) ; Vičar, Tomáš (advisor)
The goal of this thesis is to design and implement software for the detection of diabetes mellitus symptoms from the image of the human eye retina. Diabetic retinopathy is the most common disease affecting the retina. Pathologies connected with this disease can lead to partial or complete blindness. For the detection of pathological symptoms is important to correctly detect some parts of the eye retina such as optic disc and blood vessels. These can cause a problem with the identification of disease. After removing the optic disc and blood vessels, the pathology object is being detected.
Deep-learning-based pattern detection in medical images
Koščová, Zuzana ; Vičar, Tomáš (referee) ; Jakubíček, Roman (advisor)
This Bachelor thesis deals with Deep-learning-based pattern detection in medical images. For better understanding of a subject artificial neural network and convolutional neural network (CNN) are described at first. Next chapter is focused on specific detection methods which use CNN. Within a bachelor thesis a dataset of abdominal CT a MRI scans was created. Faster R-CNN and YOLO algorithms were trained and tested on acquired scans for liver detection. Implementation of chosen methods took place in Python programming language using the Pytorch library. Finally, detection results and possible use in medicine are discussed.
Cell detection using convolutional neural networks
Doskočil, Ondřej ; Chmelík, Jiří (referee) ; Vičar, Tomáš (advisor)
This bachelor thesis deals with the use of convolutional neural networks for cell detection in image data. The theoretical part contains a description of the functioning of these networks and their various architectures. In the practical part, these networks were implemented and trained on an available dataset. However, each of these networks uses a different approach to detection. Finally, the individual networks were statistically evaluated and a discussion was conducted.
Segmentation of kidney tumor in CT data
Urbanová, Hedvika ; Vičar, Tomáš (referee) ; Jakubíček, Roman (advisor)
This diploma thesis deals with the kidney tumor segmentation in CT data. First kidney anatomy and pathology is discussed. Following topics are the conventional segmentation techniques and segmentation techniques using machine learning. In the final part, the convolutional neural network is discussed as its algoritm was used for segmentation in the practical part, in which algoritm for segmentation was designed in Python programming language. This algoritm was tested and evaluated using databaze KiTS19.
Detection and measurement of electron beam in TEM images
Polcer, Simon ; Vičar, Tomáš (referee) ; Chmelík, Jiří (advisor)
This diploma thesis deals with automatic detection and measurement of the electron beam in the images from a transmission electron microscope (TEM). The introduction provides a description of the construction and the main parts of the electron microscope. In the theoretical part, there are summarized modes of illumination from the fluorescent screen. Machine learning, specifically convolution neural network U-Net is used for automatic detection of the electron beam in the image. The measurement of the beam is based on ellipse approximation, which defines the size and dimension of the beam. Neural network learning requires an extensive database of images. For this purpose, the own augmentation approach is proposed, which applies a specific combination of geometric transformations for each mode of illumination. In the conclusion of this thesis, the results are evaluated and summarized. This proposed algorithm achieves 0.815 of the DICE coefficient, which describes an overlap between two sets. The thesis was designed in Python programming language.
Detection of persons and evaluation of gender and age in image data
Dobiš, Lukáš ; Vičar, Tomáš (referee) ; Kolář, Radim (advisor)
Táto diplomová práca sa venuje automatickému rozpoznávaniu ludí v obrazových dátach s využitím konvolučných neurónových sieti na určenie polohy tváre a následnej analýze získaných dát. Výsledkom analýzy tváre je určenie pohlavia, emócie a veku osoby. Práca obsahuje popis použitých architektúr konvolučných sietí pre každú podúlohu. Sieť na odhad veku má natrénované nové váhy, ktoré sú vzápätí zmrazené a majú do svojej architektúry vložené LSTM vrstvy. Tieto vrstvy sú samostatne dotrénované a testované na novom datasete vytvorenom pre tento účel. Výsledky testov ukazujú zlepšenie predikcie veku. Riešenie pre rýchlu, robustnú a modulárnu detekciu tváre a ďalších ludských rysov z jedného obrazu alebo videa je prezentované ako kombinácia prepojených konvolučných sietí. Tieto sú implementované v podobe skriptu a následne vysvetlené. Ich rýchlosť je dostatočná pre ďalšie dodatočné analýzy tváre na živých obrazových dátach.
Monitoring system for detecting the motility and position of laboratory animals after anesthesia
Enikeev, Amir ; Vičar, Tomáš (referee) ; Čmiel, Vratislav (advisor)
This diploma thesis, entitled "Monitoring System for Determination of Motility and Position of Laboratory Animals After Anesthesia", focuses on the design and implementation of contactless detection of the position of a rat or mouse in an enclosure with a transparent cover. The aim of the semester work is to find suitable methods of realization of contactless detection of rat or mouse position and to automatically determine and display average speed or other movement characteristics. The assignment arose from the needs of animal monitoring after curative intervention and also as a necessary utility for future "shading" animal movement (automatic targeting of the scar on the animal's back). The rat, which is located inside our enclosure, is either moving as standard or is dazed after anesthesia. In this work I deal first with search of automatic monitoring systems for detection of animals in the enclosure. Then in the practical part are tested three types of cameras for visual detection of rat position and a script for automatic detection and analysis of rat movement is designed. The system works like a camera eye which in real time is able to find the area of a black box in its field of view and then limit the detection area to the size of this box and then automatically detects the center of gravity and counts. and evaluates the obtained speed with an average calculated with a test of 10 mice - voices on the screen the mouse status in the previous ten seconds. for no stressed animal The rat that is located inside our enclosure is either standard or movable after anesthesia. In this work I first deal with searches of automatic monitoring systems for detecting the position of animals in the enclosure. Then, in the practical part, I test three types of cameras for image detection of rat position. Evaluation software for motion analysis will largely be solved in the follow-up diploma thesis.Project made like monitoring and detecting software based on OpenCV.
Monitoring system for detecting the motility and position of laboratory animals after anesthesia
Enikeev, Amir ; Vičar, Tomáš (referee) ; Čmiel, Vratislav (advisor)
This diplom work entitled "Monitoring system for the detection of motility and position of laboratory animals after anesthesia" focuses on the design and implementation of non-contact detection of the rat or mouse position in the enclosure with a transparent cover. The aim of this semester paper is to find suitable methods of realizing contactless detection of the position of a laboratory rat or mouse. This automatic positioning of the animal will be used as the basis for controlling the irradiator in the next follow-up work, which will "shade" animal movement and aim at the scar on the animal's back. The rat that is located inside our enclosure is either standard or movable after anesthesia. In this work I first deal with searches of automatic monitoring systems for detecting the position of animals in the enclosure. Then, in the practical part, I test three types of cameras for image detection of rat position. Evaluation software for motion analysis will largely be solved in the follow-up diploma thesis.Project made like monitoring and detecting software based on OpenCV.

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