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Segmentation of ribs in thoracic CT scans
Kašík, Ondřej ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
This thesis deals with design and implementation of an algorithm for segmentation of ribs from thoracic CT data. For the segmentation method of rib centerlines detection is chosen. The first step of this approach is to extract the centerlines of all the bones located in the scan. These centerlines are divided into short primitives, which are subsequently classified into couple of categories, depending on whether they represent the centerline of the rib. Subsequently, the centrelines of ribs are used as the seed points of the region growing algorithm in three-dimensional space, which realizes the final segmentation of the ribs. Within the work, a database of 10 CT scans was manually annotated, which was subsequently used to validate a performance of the proposed segmentation approach. The achieved success rate of primitive classification is 96,7 %, the success rate of rib segmentation (Dice coefficient) is 86,8 %.
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Convolutional neural networks for identification of axial 2D slices in CT data
Vavřinová, Pavlína ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the classification of axial 2D slices in CT patient’s data into six categories. The sphere of convolutional neural networks was used for this purpose. For a better understanding of this issue, the basics of neural networks and then the principles of deep learning including convolutional neural networks are explained at first. The AlexNet network was specifically selected for the intention of this identification, and it was tested on the created data set after being adaptated. The overall classification success rate was 86% ,after the final adjustments, a slight improvement was achieved and the identification success rate was 87%.
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Time development analysis of treated lesion in spinal CT data
Nohel, Michal ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
This diploma thesis is focused on time-development analysis of treated lesion in CT data. The theoretical part of the thesis deals with the anatomy, physiology, and pathophysiology of the spine and vertebral bodies. It further describes diagnostic and therapeutic options for the detection and treatment of spinal lesions. It contains an overview of the current state of usage of time-development analysis in oncology. The problems of the available databases are discussed and new databases are created for subsequent analysis. Futhermore, the methodology of time-development analysis according to the shape characterization and the size of the vertebral involvement is proposed. The proposed methodological approaches to feature extraction are applied to the created databases. Their choice and suitability is discussed, including their potential for possible usege in clinical practice of monitoring the development and derivation of characteristic dependences of features on the patient's prognosis.
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Segmentation of cerebral vessels in volumetric data
Sucháček, Jan ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the design, implementation and testing of an algorithm for segmentation of cerebral vessels in 3D CT image data with a focus on stroke. First, a 3D binary mask of the brain was created and then used to extract brain tissue from the original volumetric CT images. In the extracted brain tissue, cerebral vessels were enhanced using three advanced filters based on Hessian matrix calculation and eigenvalue analysis of the Hessian matrix. The resulting parametric images of the enhanced cerebral vessels were input to six segmentation methods that were implemented and compared in this work. The program solution of this work was implemented in Matlab R2021b. The proposed algorithm was tested on real patient data. In one of the patient CT scans, cerebral vessels were labeled as part of this thesis and this resulting volume of manually segmented cerebral vessels was used to objectively evaluate the segmentation results obtained. In the theoretical part, the anatomy, physiology and pathology of the cerebral vascular supply were studied and described. Furthermore, the imaging methods used for imaging the cerebral vascular supply were described. A review of available segmentation techniques and specific approaches already published for segmentation of the cerebral vascular supply was performed. The methods used in this thesis were also theoretically described.
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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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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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Head pose estimation via stereoscopic reconstruction
Hříbková, Veronika ; Jakubíček, Roman (referee) ; Kolář, Radim (advisor)
The thesis deals with head pose estimation in stereo data. The theoretical part provides the basis for understanding the geometry of the camera, its parameters and the method of calibration. The following describes the principles of stereo analysis and creating of disparity maps. In the research section, the methods used for head pose modelling are presented and an analysis of selected published articles is given. In the course of the master’s thesis, a system of two cameras for stereoscopic acquisition of motion of the head was designed and several measurements were carried out. The obtained data was prepared for creation of disparity maps and further processing. Based on the detection of facial features, in particular the inner and outer corners of the eyes and corners of the mouth, and their correspondences, a simple geometric model in shape of triangle was created to illustrate the inclination of the facial plane in space. By computing the angle of inclination in three axes, the current head pose is obtained. Motion is modelled by tracking detected points during video sequences.
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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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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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Detection and segmentation of lumbar vertebrae in 3D CT data
Nemček, Jakub ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the detection and the segmentation of lumbar vertebrae in CT image datas. The described detection method is based on the use of a trained SVM classificator and histograms of oriented gradients as the image features. The detection method is applied on two-dimensional sagital slices of the CT image. The segmentation method is implemented as triangular mesh model deformation of models, that are obtained from averaged vertebrae in real CT datas. The first part of the thesis describes essential theoretical knowledge about the anatomy of the axial skeleton, computer tomography, image processing methods and about the detection and segmentation issues. The second part contains the algorithms realisation description, the evaluation and the discussion of the results. Applications of the algorithms in CAD systems is described at the end. The application of all of the points is done in the programming software Matlab.
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