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Segmentation of cortical parts of vertebrae
Janštová, Michaela ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
This thesis deals with a segmentation of cortical parts of vertebrae from CT image datas in programming software called MATALB. Issues about segmentation techniques are described, especially „level-set” method and its modification DRLSE. This method was chosen because of informations from articles published in spcialized publications and also thanks to its plentiful usage and satisfactory results. In the end of this paper is designed method tested on real CT datas.
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3D Slicer Extension for Tomographic Images Segmentation
Chalupa, Daniel ; Jakubíček, Roman (referee) ; Mikulka, Jan (advisor)
This work explores machine learning as a tool for medical images' classification. A literary research is contained concerning both classical and modern approaches to image segmentation. The main purpose of this work is to design and implement an extension for the 3D Slicer platform. The extension uses machine learning to classify images using set parameters. The extension is tested on tomographic images obtained by nuclear magnetic resonance and observes the accuracy of the classification and usability in practice.
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Vertebra detection and identification in CT oncological data
Věžníková, Romana ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
Automated spine or vertebra detection and segmentation from CT images is a difficult task for several reasons. One of the reasons is unclear vertebra boundaries and indistinct boundaries between vertebra. Next reason is artifacts in images and high degree of anatomical complexity. This paper describes the design and implementation of vertebra detection and classification in CT images of cancer patients, which adds to the complexity because some of vertebrae are deformed. For the vertebra segmentation, the Otsu’s method is used. Vertebra detection is based on search of borders between individual vertebra in sagittal planes. Decision trees or the generalized Hough transform is applied for the identification whereas the vertebra searching is based on similarity between each vertebra model shape and planes of CT scans.
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Image recognition for robotic hand
Labudová, Kristýna ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
This thesis concerns with processing of embedded terminals’ images and their classification. There is problematics of moire noise reduction thought filtration in frequency domain and the image normalization for further processing analyzed. Keypoints detectors and descriptors are used for image classification. Detectors FAST and Harris corner detector and descriptors SURF, BRIEF and BRISK are emphasized as well as their evaluation in terms of potential contribution to this work.
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Automatic detection of spine axis in 3D CT data
Hříbková, Veronika ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
The thesis deals with the topic of automatic detection of spine axis in 3D CT data. The theoretical part discusses the basic principles of image processing and focuses on issues of collection and processing of medical image data, particularly data from X-ray computed tomography. The entire chapter is devoted to the principle of the creation of 2D and 3D CT images. Then the article about the anatomy of the axial skeleton is elaborated focusing on the anatomy of the spine. The second part is devoted to the proposal and implementation of methods of segmentation of the spine using a simple thresholding and morphological operations to locate the initial position of the spine axis with the definition of the beginning and the end of the axis using cross-correlation with created binar masks. Subsequent refinement of the position of the spine is done by segmentation of the spinal canal using a region growing method, when the starting points, located inside the spinal canal, are acquired by finding the centre of gravity of a vertebra or by Hough transform.
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Texture analysis of tumor tissue in lung CT data.
Šalplachta, Jakub ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
The aim of this work is the revelation of the possibility of the use of texture analysis methods to detection and segmentation tumor tissue in lung CT image data and classification viable areas of tumor tissue. The main assumption of this thesis are differences of textural features between tumor and surrounding tissues and changes of these properties during development and treatment of this disease. The thesis contains overview of texture analysis methods. It deals with the creation of own method which is composed of some methods of texture analysis that create vector of properties (for each voxel in the image we get vector of features). This vector is afterwards processed by methods of cluster analysis. Content of this work is theoretical research of this issue, description of own method and statistical evaluation of the results. The method is processed in programming environment Matlab®.
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Medical image segmentation based on graph cut with shape prior
Kozlová, Dominika ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
This thesis deals with a graph-based image segmentation and its improvement by using the information about the shape of the object for creating specific graph architecture (template). There are described basics of the graph theory, which is the basis of the graph segmentation methods. Designed segmentation algorithm was realized in 2D with graphical user interface in MATLAB. For segmentation of volume data, the method was extended into 3D. Implemented method was tested on simulated data and on real CT and MRI images of vertebra and brain. Obtained results were evaluated and compared with the original method without using the template.
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Adaptive image sharpening
Jakubíček, Roman ; Odstrčilík, Jan (referee) ; Jan, Jiří (advisor)
This thesis is the analysis of the problem and technical report that supports a computer program for enhancement image. The main objective of picture processeng is adaptive sharpening, which is obtained by the application of a local convolutional operator. The decision on the degree of sharpening at individual pixels is based on the value of the local standard deviation of brightness. The degree of sharpening can take binary or continuous values. The first part of the report briefly discusses the theory of adaptive image sharpening. Knowledge of this theory is necessary for understanding the remaining chapters, which describe the individual algorithms including flows-diagrams, implementation of the program and graphical enviroment and also assess the achieved results, including demonstration on examples. The last section of the report deals with variability of images and it’s influence on settings parameters of sharpening.
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Ultrasound image registration based on active contours
Hesko, Branislav ; Jakubíček, Roman (referee) ; Harabiš, Vratislav (advisor)
This diploma thesis aims to implement an active contour method for ultrasound image segmentation. Properties of ultrasound images, basic segmentation approaches and a~principle of choosen active contour methods are described within theoretical part. Two different groups of active contour methods exists, methods with use of gradient and without use of gradient as image feature. For comparision, one method of each group is implemented in practical part and subsequently, segmentation efficiency and properties of methods are compared in evaluation part.
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Movement correction in thoracic dynamic contrast CT data
Jakubíček, Roman ; Malínský, Miloš (referee) ; Walek, Petr (advisor)
This thesis deals with a nonrigid image registration for movement correction in thoracic dynamic contrast CT data. The deformation field is initialized by the analysis of disparities based on nonlinear matched filter, which defines local movement deformation. The values of control points are optimized by the Nelder-Mead method. The transformation model is based on a 4D (3D + time) free-form B-spline deformation for feature of movement distortion. The first part of the thesis briefly discusses the theory of image registration. Knowledge of this theory is necessary for understanding the remaining chapters, which describe the proposed method and its realization. The large part of this thesis is devoted to the geometrical image transformations, that is very important for the image registration. The thesis also describes a simplex method for function minimization. Three publicated methods of registration of medical 4D CT data are given. In the following chapter are individual parts of the purposed nonrigid registration including possible problems and their solution described.
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