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Textural Analysis of Nerve Fibre Layer in Retinal Images
Novotný, Adam ; Jan, Jiří (referee) ; Odstrčilík, Jan (advisor)
This work describes completely new approach to detection of retinal nerve fibre layer (RNFL) loss in colour fundus images. Such RNFL losses indicate eye glaucoma illness and an early diagnosis of RNFL changes is very important for successful treatment. Method is presented with the purpose of supporting glaucoma diagnosis in ophthalmology. The proposed textural analysis method utilizes local binary patterns (LBP). This approach is characterized especially by computational simplicity and insensitivity to monotonic changes of illumination. Image histograms of LBP distributions are used to gain several textural features aimed to classify healthy or glaucomatous tissue of the retina. The method was experimentally tested using fundus images of glaucomatous patients with focal RNFL loss. The results show that the proposed method can be used in order to supporting diagnosis of glaucoma with satisfactory efficiency.
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Analysis of Retinal Images Aimed to Nerve Fiber Layer Detection
Spáčil, Michal ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
Goal of this work is to theoretically develop and then program a system in Matlab environment to be used as a detection tool for layer of retinal neuron pathways . First part engages oneself upon the problem of analysis within spectral plane and results of using filters conceived upon statistical occurrences of certain frequencies in used samples. Second part than deals with use of gabor filters to detect neuron pathways and the statistical results gained by their use. Based on the results an analysis tool was programmed.
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Texture analysis
Opletal, Tomáš ; Kratochvíla, Lukáš (referee) ; Honec, Peter (advisor)
Diploma thesis discusses texture analysis. Goal is to detect anomalies on the material, which could arise during process of making calenders on the nonwoven material. This thesis is using methods of Fourier transform with statistics and Neural networks to detect position of the missing calender.
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Image analysis of object surface texture
Klimeš, Jiří ; Horák, Karel (referee) ; Petyovský, Petr (advisor)
This master thesis deals with design and implementation of algorithms for image analysis of object surface texture for the purpose of automating the surface grinding process. In the first part of this thesis, a search was performed in the field of image analysis of object surface texture. The proposed descriptors were tested on the created annotated database of texture images. Subsequently, a scene for image acquisition of the machined object was designed and assembled, and the grinding process was automated based on the results of the previous analysis. The implementation and achieved results were evaluated and other possible improvements were proposed.
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Texture modeling applied to medical images
Remeš, Václav ; Haindl, Michal (advisor)
and contributions This thesis presents novel descriptive multidimensional Markovian textural models applied to computer aided diagnosis in the field of X-ray mammogra- phy. These general mathematical models, applicable in wide areas of texture modeling outside X-ray mammography as well, provide ideal visual verification using synthesis of the corresponding measured data spaces, contrary to stan- dard discriminative models. All achieved results in the thesis are extensively benchmarked. The thesis presents two methods for breast density classification in X-ray mammography. The methods were tested on the widely known MIAS database and the state-of-the art INbreast database, with competitive results. Several methods for completely automatic mammogram texture enhance- ment are presented. These methods are based on the descriptive textural mod- els developed in the thesis which automatically adapt to the analyzed X-ray texture, thus being universal for any type of input without the need of further manual tuning of specific parameters. The methods' outputs highlight regions of interest, detected as textural abnormalities. The methods provide the pos- sibility of enhancement tuned to specific types of mammogram tissue. Hence, the enhanced mammograms can help radiologists to decrease their false negative...
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Automatic Segmentation And Classification Of Internal Calibration Tissues
Šalplachta, Jakub
The aim of this work is finding a way to make calculation of bone mineral density of vertebrae with the use of internal calibration tissues fully automatic procedure. To accomplish that several methods for segmentation and classification of paraspinal muscle and subcutaneous fat were tested. For the testing and learning procedure of this work manually labelled database of tissues of interest was created and medically verified.
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Texture modeling applied to medical images
Remeš, Václav ; Haindl, Michal (advisor)
and contributions This thesis presents novel descriptive multidimensional Markovian textural models applied to computer aided diagnosis in the field of X-ray mammogra- phy. These general mathematical models, applicable in wide areas of texture modeling outside X-ray mammography as well, provide ideal visual verification using synthesis of the corresponding measured data spaces, contrary to stan- dard discriminative models. All achieved results in the thesis are extensively benchmarked. The thesis presents two methods for breast density classification in X-ray mammography. The methods were tested on the widely known MIAS database and the state-of-the art INbreast database, with competitive results. Several methods for completely automatic mammogram texture enhance- ment are presented. These methods are based on the descriptive textural mod- els developed in the thesis which automatically adapt to the analyzed X-ray texture, thus being universal for any type of input without the need of further manual tuning of specific parameters. The methods' outputs highlight regions of interest, detected as textural abnormalities. The methods provide the pos- sibility of enhancement tuned to specific types of mammogram tissue. Hence, the enhanced mammograms can help radiologists to decrease their false negative...
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Analysis of 3D CT image data aimed at detection and classification of specific tissue structures
Šalplachta, Jakub ; Malínský, Miloš (referee) ; Jan, Jiří (advisor)
This thesis deals with the segmentation and classification of paraspinal muscle and subcutaneous adipose tissue in 3D CT image data in order to use them subsequently as internal calibration phantoms to measure bone mineral density of a vertebrae. Chosen methods were tested and afterwards evaluated in terms of correctness of the classification and total functionality for subsequent BMD value calculation. Algorithms were tested in programming environment Matlab® on created patient database which contains lumbar spines of twelve patients. Following sections of this thesis contain theoretical research of the issue of measuring bone mineral density, segmentation and classification methods and description of practical part of this work.
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Automation of Exoscopic Analysis Using Image Processing of Sedimentary Grains Acquired by Electron Microscope
Křupka, Aleš ; Křížek,, Marek (referee) ; Baroňák, Ivan (referee) ; Říha, Kamil (advisor)
This thesis deals with image analysis methods which can be exploited in exoscopic analysis of sedimentary grains, specifically for the purpose of distinguishing between geomorphologic geneses which influenced a form of sedimentary grains. The images of sedimentary grains were acquired by a scanning electron microscope. The main contribution is the proposal of multiple methods that can significantly automate the exoscopic analysis. These methods cover the automatic segmentation of grains in image, the automatic analysis of roundness of 2D grain projection and the classification of geomorphologic geneses according to the grain surface structure. In the section concerning the automatic segmentation, a segmentation method enabling an easy subsequent manual result correction was proposed. This method is based on the split-and-merge approach. The individual steps the procedure were designed to exploit specific properties of sedimentary grain images in order to obtain the best segmentation results. In the section concerning the automatic roundness analysis of 2D projection of sedimentary grains, an influence of pixel resolution on a result roundness value was evaluated. Further, a minimal number of grains, which is necessary to analyze in order to reliably compare a pair of geomorphological geneses, was investigated. For the determination of this number, a method was proposed and experimentally verified. In the section of automatic analysis of sedimentary grain surface structure, a method for classification of geomorphologic geneses was proposed. The method utilizes low-level texture features which describes individual images of sedimentary grains. A model of geomorphological genesis is constituted of a set of histograms representing occurrences of different configurations of low-level texture features. The methods proposed in the thesis were tested and evaluated based on a database, which consists of sedimentary grain samples from 4 different geomorphological geneses (eolic, glacial, slope and volcanic).
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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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