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Intracranial hemorrhage localization in axial slices of head CT images
Kopečný, Kryštof ; Chmelík, Jiří (referee) ; Nemček, Jakub (advisor)
This thesis is focused on detection of intracranial hemorrhage in CT images using both one-stage and two-stage object detectors based on convolutional neural networks. The fundamentals of intracranial hemorrhage pathology and CT imaging as well as essential insight into computer vision and object detection are listed in this work. The knowledge of these fields of studies is a starting point for the implemenation of hemorrhage detector. The use of open-source CT image datasets is also discussed. The final part of this thesis is a model evaluation on a test dataset and results examination.
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Detection of foreign objects in X-ray chest images using machine learning methods
Matoušková, Barbora ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
Foreign objects in Chest X-ray (CXR) cause complications during automatic image processing. To prevent errors caused by these foreign objects, it is necessary to automatically find them and ommit them in the analysis. These are mainly buttons, jewellery, implants, wires and tubes. At the same time, finding pacemakers and other placed devices can help with automatic processing. The aim of this work was to design a method for the detection of foreign objects in CXR. For this task, Faster R-CNN method with a pre-trained ResNet50 network for feature extraction was chosen which was trained on 4 000 images and lately tested on 1 000 images from a publicly available database. After finding the optimal learning parameters, it was managed to train the network, which achieves 75% precision, 77% recall and 76% F1 score. However, a certain part of the error is formed by non-uniform annotations of objects in the data because not all annotated foreign objects are located in the lung area, as stated in the description.
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Identification of vertebrae type in CT data by machine learning methods
Matoušková, Barbora ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
Identification of vertebrae type by machine learning is an important task to facilitate the work of medical doctors. This task is embarrassed by many factors. First, a spinal CT imagining is usually performed on patiens with pathologies such as lesions, tumors, kyphosis, lordosis, scoliosis or patients with various implants that cause artifacts in the images. Furthermore, the neighboring vertebraes are very similar which also complicates this task. This paper deals with already segmented vertebrae classification into cervical, thoracic and lumbar groups. Support vector machines (SVM) and convolutional neural networks (CNN) AlexNet and VGG16 are used for classification. The results are compared in the conclusion.
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Methods of Detection, Segmentation and Classification of Difficult to Define Bone Tumor Lesions in 3D CT Data
Chmelík, Jiří ; Flusser,, Jan (referee) ; Kozubek, Michal (referee) ; Jan, Jiří (advisor)
The aim of this work was the development of algorithms for detection segmentation and classification of difficult to define bone metastatic cancerous lesions from spinal CT image data. For this purpose, the patient database was created and annotated by medical experts. Successively, three methods were proposed and developed; the first of them is based on the reworking and combination of methods developed during the preceding project phase, the second method is a fast variant based on the fuzzy k-means cluster analysis, the third method uses modern machine learning algorithms, specifically deep learning of convolutional neural networks. Further, an approach that elaborates the results by a subsequent random forest based meta-analysis of detected lesion candidates was proposed. The achieved results were objectively evaluated and compared with results achieved by algorithms published by other authors. The evaluation was done by two objective methodologies, technical voxel-based and clinical object-based ones. The achieved results were subsequently evaluated and discussed.
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Utilisation of shape analysis methods for object classification in medical images
Karela, Jiří ; Odstrčilík, Jan (referee) ; Chmelík, Jiří (advisor)
Bachelor thesis deals with problems of shape analysis. It describes some procedures and methods related to this kind of analysis. The thesis is divided into theoretical part, practical part and conclusion. In the theoretical part we describe in greater detail some methods, with the help of which the practical part was solved. But other theories related to the topic are also described. The practical part then follows the given theory and solves the problem of shape analysis due to the knowledge gained in the theory. The algorithm is tested on medical data from CT of vertebrae. The conclusion serves as a summary and evaluation of the shape analysis solution. It also serves as a reflection on the realization of our method, ie how our solution and result could be improved.
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Thrombi detection in main brain arteries in CT image data
Líška, Martin ; Nemček, Jakub (referee) ; Chmelík, Jiří (advisor)
The master’s thesis deals with automatic preprocessing, segmentation and consecutive analysis of volume data of anonymized patient CTA acquisitions with an indication of stroke. Preprocessing of volume data is an essential step for proper vascular tree segmentation and analysis. The region growing method was used to segment the vascular tree of the brain. After extracting the vascular tree, the labeling of individual branches was applied in the algorithm and the appropriate features were extracted. The analysis examined the features of vessel lengths, their diameter and local brightness profiles, which are important indicators of possible stenosis or occlusion of the main vessels of the brain. The output of the algorithm are various modalities of diagnostic, assisted visualizations of the segmented vascular tree. The segmentation and analysis algorithm of cerebrovascular system was created in the MATLAB programming environment.
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Detection and evaluation of distorted frames in retinal image data
Vašíčková, Zuzana ; Chmelík, Jiří (referee) ; Kolář, Radim (advisor)
Diplomová práca sa zaoberá detekciou a hodnotením skreslených snímok v retinálnych obrazových dátach. Teoretická časť obsahuje stručné zhrnutie anatómie oka a metód hodnotenia kvality obrazov všeobecne, ako aj konkrétne hodnotenie retinálnych obrazov. Praktická časť bola vypracovaná v programovacom jazyku Python. Obsahuje predspracovanie dostupných retinálnych obrazov za účelom vytvorenia vhodného datasetu. Ďalej je navrhnutá metóda hodnotenia troch typov šumu v skreslených retinálnych obrazoch, presnejšie pomocou Inception-ResNet-v2 modelu. Táto metóda nebola prijateľná a navrhnutá bola teda iná metóda pozostávajúca z dvoch krokov - klasifikácie typu šumu a následného hodnotenia úrovne daného šumu. Pre klasifikáciu typu šumu bolo využité filtrované Fourierove spektrum a na hodnotenie obrazu boli využité príznaky extrahované pomocou ResNet50, ktoré vstupovali do regresného modelu. Táto metóda bola ďalej rozšírená ešte o krok detekcie zašumených snímok v retinálnych sekvenciách.
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Segmentation of amyloid plaques in brains of trangenic rats based on microCT image data
Kačníková, Diana ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
The presence of amyloid plaques in the hippocampus highlights the incidence of Alzheimer’s disease. Manual segmentation of amyloid plaques is very time consuming and increases the time that can be used to monitor the distribution of amyloid plaques. Distribution carries significant information about disease progression and the impact of potential therapy. The automatic or semi-automatic segmentation method can lead to significant savings in the time which are required when the disease has rapid progression. The description of amyloid plaques and the computed tomography are included in this work. In this diploma thesis are three implemented algorithms, two of them are based on published articles and one’s own methodological solution. The conclusion of the thesis is a quantitative evaluation of the accuracy of implemented segmentation procedures.
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Segmentation of hippocampus in MRI data
Kodym, Oldřich ; Chmelík, Jiří (referee) ; Walek, Petr (advisor)
The thesis deals with application of graph-based methods in segmentation of low contrast image data, specifically hippocampus segmentation from magnetic resonance data. Firstly, basics and terminology of graph theory is introduced. Next, minimum graph cut method is explained along with algorithms capable of finding this cut. After that comes the description of its implementation for 2D and 3D image data segmentation. Method was tested on sample data and then implemented as a 3D Slicer software module. Here the method was tested on the hipocampus data of healthy patients as well as patients suffering from Alzheimer’s disease. Most common problems occuring during the segmentation were forshadowed as well as possible ways to solve them.
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Engineering design of conveyor
Chmelík, Jiří ; Čípek, Pavel (referee) ; Foltýnová, Dana (advisor)
The bachelor thesis deals with the design of a conveyor for transporting welded beams with variable dimensions. At the beginning of the work are listed some conveyors, which are commonly used in practice. Following analysis of available design solutions, design designs of equipment, their description and based on comparison was chosen the most suitable design solution. In the course of work is performed safety analysis of selected nodes. At the end of the work is evaluated the whole construction and elaborated the drawing documentation of the device. The proposed solution can also be used to transport parts of similar shapes.
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