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Creating 3D Model of Temporomandibular Joint
Šmirg, Ondřej ; Bartušek, Karel (referee) ; Liberda,, Ondřej (referee) ; Smékal, Zdeněk (advisor)
The dissertation thesis deals with 3D reconstruction of the temporomandibular joint from 2D slices of tissue obtained by magnetic resonance. The current practice uses 2D MRI slices in diagnosing. 3D models have many advantages for the diagnosis, which are based on the knowledge of spatial information. Contemporary medicine uses 3D models of tissues, but with the temporomandibular joint tissues there is a problem with segmenting the articular disc. This small tissue, which has a low contrast and very similar statistical characteristics to its neighborhood, is very complicated to segment. For the segmentation of the articular disk new methods were developed based on the knowledge of the anatomy of the joint area of the disk and on the genetic-algorithm-based statistics. A set of 2D slices has different resolutions in the x-, y- and z-axes. An up-sampling algorithm, which seeks to preserve the shape properties of the tissue was developed to unify the resolutions in the axes. In the last phase of creating 3D models standard methods were used, but these methods for smoothing and decimating have different settings (number of polygons in the model, the number of iterations of the algorithm). As the aim of this thesis is to obtain the most precise model possible of the real tissue, it was necessary to establish an objective method by which it would be possible to set the algorithms so as to achieve the best compromise between the distortion and the model credibility achieve.
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3D Objects Reconstruction from Image Data
Cír, Filip ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
This paper deals with 3D reconstruction of objects from image data. There is describes theoretical basis of the 3D optical scanning. Handheld 3D optical scanner setup is described composed of a single camera and a line laser whose position is fixed with respect to the camera. Set of image markers and a simple real-time detection algorithm are proposed. Detected markers are used to estimate position and orientation of the camera. Finally, laser detection and triangulation of points lying on object surface are discussed.
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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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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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Analysis of volumetric change of Hippocampus caused by Alzheimer's disease
Pham, Minh Tuan ; Harabiš, Vratislav (referee) ; Walek, Petr (advisor)
Interest in hippocampus increased sharply after his significance in the process of learning and retention of information was published. In particular, considerable interest was in its volume changes and their effect on Alzheimer’s disease. Understanding the structure and function hippocampus would contribute to a more accurate diagnosis of this disease. In this work was created a method of hippocampal segmentation using active contours. With its help, the data composed of both healthy and a diseased patients was segmented and the results were then statistically analyzed using statistical methods such as Kruskal-Walis test, Mann-Whitney test. The level of significance given by results of analysis supports alternative hypothesis that attaches significance of the difference in volume of the hippocampus between studied groups.
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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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Computer Aided Recognization and Classification of Coat of Arms
Vídeňský, František ; Kočí, Radek (referee) ; Zbořil, František (advisor)
This master thesis describes the design and development of the system for detection and recognition of whole coat of arms as well as each heraldic parts. In the thesis are presented methods of computer vision for segmentation and detection of an object and selected methods that are the most suitable. Most of the heraldic parts are segmented using a convolution neural networks and the rest using active contours. The Histogram of the gradient method was selected for coats of arms detection in an image. For training and functionality verification is used my own data set. The resulting system can serve as an auxiliary tool used in auxiliary sciences of history.
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Segmentation Methods in Biomedical Image Processing
Mikulka, Jan ; Přibil, Jiří (referee) ; Dostál, Otto (referee) ; Gescheidtová, Eva (advisor)
The PhD thesis deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It is focused primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR) and microscopic images of tissues. It is easy to describe edges of the sought objects using of segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown in this thesis. Research in the area of image segmentation is focused on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. The results of the thesis are methods proposed for automatic image segmentation and classification.
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Segmentation of the cord canal and intervertebral discs in MRI data
Koban, Martin ; Odstrčilík, Jan (referee) ; Jakubíček, Roman (advisor)
The concern of this thesis is development of the method for the spinal canal and intervertebral discs segmentation in volume MRI data. The primary aim is to achieve the highest possible level of automation and accuracy allowing for reliable quantitative evaluation of the results. The algorithm is based on the random walk model in combination with a specific active contour method formulated through level set concept. The proposed approach is tested using a database of three-dimensional T2-weighted MR images, which also contains referential manual segmentation of intervertebral discs.
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Object selection in raster image
Fiala, Ondřej ; Pelikán, Josef (advisor) ; Kolomazník, Jan (referee)
A common task solved during a work with raster images is a object selection. Results obtained from this task are used in a number of applications - from analysis of the growth of human settlements to automatic medical diagnosis. This work describes in detail the object selection in raster images and focuses on active contours models. This work also consider extending the graphic program GIMP, bringing a new plug-in for object selection in raster images. The solution is based on the original classic model of active contours, the extended model using a gradient vector field (GVF) and proposed improvements. The work presents results obtained by this tool and brings recommendations for future development of active contours models.
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