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Real-Time Image Processing Algorithm Implementation
Rypák, Andrej ; Zemčík, Pavel (referee) ; Černocký, Jan (advisor)
This Bachelor's Thesis was performed during a study stay at the École Supérieure d'Ingénieurs en Électronique et Électrotechnique Paris, France. In this project, I deal with designing program components for a rapid prototyping software called SynDEx that is used for automatic code generation, real-time application development and optimization for multicomponent mixed architectures. I briefly introduce this software. I present techniques I used to implement two different specific image processing chains and a displaying component. The chains perform image segmentation and labelling.In this project I deal with designing program components for a rapid prototyping software called SynDEx that is used for automatic code generation, real-time application development and optimization for multicomponent mixed architectures. I briefly introduce this software. I present techniques I used to implement two different specific image processing chains and a displaying component. The chains perform image segmentation and labelling.
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Automatic detection of microcalcifications in mammogram images
Hývlová, Denisa ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
This bachelor thesis is focused on detection of microcalcification in mammography images. The introduction describes connection between their presence and breast cancer, principle of mammography and the DICOM standard used in radiology. In the following part the methods used for microcalcification enhancement and segmentation are explained. Detection algorithm based on wavelet transform, morphological closing and thresholding was designed in MATLAB. For evaluation of the results a graphical user interface was developed and an algorithm for automatic evaluation of the success rate in annotated mammography database was implemented.
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"Sci-Fi" Music Library
Holas, Jan ; Šolony, Marek (referee) ; Polok, Lukáš (advisor)
This thesis deals with usage of computer vision as a way of interaction between human and computer. It introduces implementation of music library and audio player which is controlled by showing CD jewel cases of music albums and audio player paper control cards on a camera connected to a computer. This thesis describes algorithms for segmentation of an object from scene based on object's rectangular shape and matching that image with image database (music album database) using SURF feature detector. In conclusion, it summarizes achieved results and mentions some ideas and possibilities of further development.
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Segmentation of the kidney from the renal perfusion MR image sequences
Jína, Miroslav ; Walek, Petr (referee) ; Malínský, Miloš (advisor)
This master’s thesis deals with kidney segmentation in perfusion magnetic resonance image sequences. Kidney segmentation is carry out by a few methods such as regionbased techniques, deformable models, specimen-based methods, edge-oriented methods etc. The universal algorithm for patient kidney segmentation still does not exist. Proposed method is an active contour Snake, which is created in programming environment MatLab. Final contours are quantitatively and visually compared to manual kidney segmentation.
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Advanced retinal vessel segmentation methods in colour fundus images
Svoboda, Ondřej ; Jan, Jiří (referee) ; Odstrčilík, Jan (advisor)
Segmentation of vasculature tree is an important step of the process of image processing. There are many methods of automatic blood vessel segmentation. These methods are based on matched filters, pattern recognition or image classification. Use of automatic retinal image processing greatly simplifies and accelerates retinal images diagnosis. The aim of the automatic image segmentation algorithms is thresholding. This work primarily deals with retinal image thresholding. We discuss a few works using local and global image thresholding and supervised image classification to segmentation of blood tree from retinal images. Subsequently is to set of results from two different methods used image classification and discuss effectiveness of the vessel segmentation. Use image classification instead of global thresholding changed statistics of first method on healthy part of HRF. Sensitivity and accuracy decreased to 62,32 %, respectively 94,99 %. Specificity increased to 95,75 %. Second method achieved sensitivity 69.24 %, specificity 98.86% and 95.29 % accuracy. Combining the results of both methods achieved sensitivity up to72.48%, specificity to 98.59% and the accuracy to 95.75%. This confirmed the assumption that the classifier will achieve better results. At the same time, was shown that extend the feature vector combining the results from both methods have increased sensitivity, specificity and accuracy.
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Interactive Medical Image Segmentation
Olša, Martin ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
This work deals with a fast level-set approach for segmentation of anatomical structures in volumetric medical images. The fast level-set method evolves a closed 3D surface in time propagating the surface form an initial position. The major contribution of this work is the implementation of the level-set method and construction of an interactive tool for segmentation of 3D medical data using this method. The tool is able to interactively change parameters of the evolution during the segmentation process itself. Due to the nature of level-set method, the evolution process can be stopped at any time, or backtracked and restarted from any previous step with a different configuration.
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Segmetation of tomographic data in 3D Slicer
Korčuška, Robert ; Dvořák, Pavel (referee) ; Mikulka, Jan (advisor)
This thesis contains basic theoretical information about SVM-based image segmentation and data classification. Basic information about 3D Slicer software are presented. Aspects of medical images segmentation are described. Workplan and implemetation of SVM method for MRI segmentation in 3D Slicer sofware as extension module is created. SVM method is compared with simple segmentation algorithms included in 3D Slicer. Quality of segmentation, based on SVM, tested on real subjects is experimentaly demonstrated.
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Image segmentation using graph neural networks
Boszorád, Matej ; Kolařík, Martin (referee) ; Myška, Vojtěch (advisor)
This diploma thesis describes and implements the design of a graph neural network usedfor 2D segmentation of neural structure. The first chapter of the thesis briefly introduces the problem of segmentation. In this chapter, segmentation techniques are divided according to the principles of the methods they use. Each type of technique contains the essence of this category as well as a description of one representative. The second chapter of the diploma thesis explains graph neural networks (GNN for short). Here, the thesis divides graph neural networks in general and describes recurrent graph neural networks(RGNN for short) and graph autoencoders, that can be used for image segmentation, in more detail. The specific image segmentation solution is based on the message passing method in RGNN, which can replace convolution masks in convolutional neural networks.RGNN also provides a simpler multilayer perceptron topology. The second type of graph neural networks characterised in the thesis are graph autoencoders, which use various methods for better encoding of graph vertices into Euclidean space. The last part ofthe diploma thesis deals with the analysis of the problem, the proposal of its specific solution and the evaluation of results. The purpose of the practical part of the work was the implementation of GNN for image data segmentation. The advantage of using neural networks is the ability to solve different types of segmentation by changing training data. RGNN with messaging passing and node2vec were used as implementation GNNf or segmentation problem. RGNN training was performed on graphics cards provided bythe school and Google Colaboratory. Learning RGNN using node2vec was very memory intensive and therefore it was necessary to train on a processor with an operating memory larger than 12GB. As part of the RGNN optimization, learning was tested using various loss functions, changing topology and learning parameters. A tree structure method was developed to use node2vec to improve segmentation, but the results did not confirman improvement for a small number of iterations. The best outcomes of the practical implementation were evaluated by comparing the tested data with the convolutional neural network U-Net. It is possible to state comparable results to the U-Net network, but further testing is needed to compare these neural networks. The result of the thesisis the use of RGNN as a modern solution to the problem of image segmentation and providing a foundation for further research.
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Analysis of autofluorescence retinal images
Mosyurchak, Andriy ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
Autofluorescence retinal images are obtained with a confocal laser scanning ophthalmoscope, and used for the diagnostic of glaucoma. Glaucoma causes a gradual death of nerve cells and can cause blindness. Retina autofluorescence is caused by pigment lipofuscin, which causes cell damage. The aim of this work was to study methods suitable for segmentation of autofluorescence zones and method for tracking objects in an image. In this project was implemented algorithm of autofluorescence zone detection using method of region growing, designed and realized method for tracking autofluorescence regions.
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