|
|
Detection and Vizualization of Features in a Point Cloud
Kratochvíl, Jiří Jaroslav ; Mikeš, Josef (referee) ; Martišek, Dalibor (referee) ; Procházková, Jana (advisor)
The point cloud is an unorganized set of points with 3D coordinates (x, y, z) which represents a real object. These point clouds are acquired by the technology called 3D scanning. This scanning technique can be done by various methods, such as LIDAR (Light Detection And Ranging) or by utilizing recently developed 3D scanners. Point clouds can be therefore used in various applications, such as mechanical or reverse engineering, rapid prototyping, biology, nuclear physics or virtual reality. Therefore in this doctoral Ph.D. thesis, I focus on feature detection and visualization in a point cloud. These features represent parts of the object that can be described by the well--known mathematical model (lines, planes, helices etc.). The points on the sharp edges are especialy problematic for commonly used methods. Therefore, I focus on detection of these problematic points. This doctoral Ph.D. thesis presents a new algorithm for precise detection of these problematic points. Visualization of these points is done by a modified curve fitting algoritm with a new weight function that leads to better results. Each of the proposed methods were tested on real data sets and compared with contemporary published methods.
|
|
|
Blood vessel tree segmentation of the mouse liver in CT data
Smékalová, Veronika ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
The methodology of visualization of soft tissue is in biology and medicine a topic for many years. During this period there were approving many techniques how to achieve accurate and authentic image of the researched object or structure. X-ray computed tomography is very helpful to get this goal but is necessary to improve contrasting techniques as well as the techniques of image post-processing. This thesis deals with imaging soft tissue. Specifically, it focuses on mouse liver contrasting with the artificial resin Microfil. Thesis also describes image processing technique (thresholding and region growing) for the data of the measurement with the goal of the visualization of the sample in 3D.
|
|
|
Segmentation of 3D medical images based on region growing method
Kantorová, Martina ; Krátká, Lucie (referee) ; Harabiš, Vratislav (advisor)
This bachalor thesis deals with a region growing approach for segmentation of volumetric medical images. The aim is to present basic methods of segmentation of image data and to focus in particular on the approach of region growing. The input data are brain slices of magnetic resonance imaging which can be visualized using the browser into the three basic planes. The viewer is implemented in MATLAB programming environment. Image segmentation is realized by seeded region growing.
|
|
| |
|
|
Segmentation of the basic parts of human brain in MR data
Klásek, Pavel ; Jiřík, Radovan (referee) ; Malínský, Miloš (advisor)
This work describes segmentation methods used in image data processing, from which there are selected and implemented suitable methods for solving the assignment of segmentation parts of human brain – region growing and watershed algorithm. Segmentation techniques are realized on real data sources. Final segmentation results are presented, compared and evaluated accordig to the advanced software FreeSurfer segmentation results. In addition there is a list of available software that can be applied for the purpose of neurological image segmentation.
|
|
|
Object identification
Fábry, Tomáš ; Gogol, František (referee) ; Richter, Miloslav (advisor)
Work describes creation and functionality of created program for object recognition. Program issue from snapshot from webcam and given sample of searched object. It recognize all objects on the snapshot and marks those similar to given sample with aberrations to it. Program is created as an aplication for windows with language C/C++. For comunication with webcam and displaying results a used functions from library OpenCV. In work is shown structure of program and arrangement of data. Next are decribed most important created functions and used OpenCV functions. With them there is explained used technqiues from object recognition field and image processing. Program enviroment and options are described.
|
|
|
Design of a New Method for Stereovision
Kopečný, Josef ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
This thesis covers with the problems of photogrammetry. It describes the instruments, theoretical background and procedures of acquiring, preprocessing, segmentation of input images and of the depth map calculating. The main content of this thesis is the description of the new method of stereovision. Its algorithm, implementation and evaluation of experiments. The covered method belongs to correlation based methods. The main emphasis lies in the segmentation, which supports the depth map calculation.
|
|
|
Segmentation of magnetic resonance images
Hanák, Pavel ; Harabiš, Vratislav (referee) ; Havlíček, Martin (advisor)
Segmentation, magnetic resonance, grey matter, white matter, thresholding, region growing, splitting and merging, watershed, level set method.
|
|
|
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.
|
|
|
Analysis of cytology images
Pavlík, Jan ; Blaha, Milan (referee) ; Kolář, Radim (advisor)
This master’s thesis is focused on automating the process of differential leukocyte count in peripherial blood using image processing. It deals with the design of the processing of digital images - from scanning and image preprocessing, segmentation nucleus and cytoplasm, feature selection and classifier, including testing on a set of images that were scanned in the context of this work. This work introduces used segmentation methods and classification procedures which separate nucleus and the cytoplasm of leukocytes. A statistical analysis is performed on the basis of these structures. Following adequate statistical parameters, a set of features has been chosen. This data then go through a classification process realized by three artificial neural networks. Overall were classified 5 types of leukocytes: neutropfiles, lymphocytes, monocytes, eosinophiles and basophiles. The sensitivity and specificity of the classification made for 4 out of 5 leukocyte types (neutropfiles, lymphocytes, monocytes, eosinophiles) is higher than 90 %. Sensitivity of classiffication basophiles was evaluated at 75 % and specificity at 67 %. The total ability of classification has been tested on 111 leukocytes and was approximately 91% successful. All algorithms were created in the MATLAB program.
|
guest ::
