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Preprocessing of retinal images aimed at support diagnosis of glaucoma
Holásková, Anna ; Walek, Petr (referee) ; Odstrčilík, Jan (advisor)
Preprocessing of retinal images can serve as a first phase of the further image analysis or the first step preceding diagnosing of various eye diseases. The preprocessing thus represents methods of image adjustments that can improve visual characteristics of fundus images. These methods mainly include the removal of noise generated during data acquisition, contrast and brightness transformations, edge detection and thresholding. This work handles with the basic methods of image preprocessing and specific methods of preprocessing of retinal images. The preprocessing includes global illumination correction, high-pass and homomorphic filtering and adaptive enhancement of the images. Manual methods for fundus image preprocessing that are usually based on the doctor's experience can be used as well. Hence, a procedure for enhancement of retinal images using Adobe Photoshop is mentioned in this work too. Three methods for preprocessing of fundus images were selected and implemented in MATLAB programming software. These methods include homomorphic filtering, CLAHE (Contrast Limited Adaptive Histogram Equalization) and adaptive enhancement. Experimental program functions were created and tested on the available image data. Results of the selected methods are mentioned in the conclusion section. Instructions for use of implemented functions are in appendix.
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Analysis of retinal nerve fiber layer for diagnosis of glaucoma
Vodáková, Martina ; Malínský, Miloš (referee) ; Odstrčilík, Jan (advisor)
The master thesis is focused on creating a methodology for quantification of the nerve fiber layer on photographs of the retina. The introductory part of the text presents a medical motivation of the thesis and mentions several studies dealing with this issue. Furthermore, the work describes available textural features and compares their ability to quantify the thickness of the nerve fiber layer. Based on the described knowledge, the methodology to make different regression models enabling prediction of the retinal nerve fiber layer thickness was developed. Then, the methodology was tested on the available image dataset. The results showed, that the outputs of regression models achieve a high correlation between the predicted output and the retinal nerve fiber layer thickness measured by optical coherence tomography. The conclusion discusses an usability of the applied solution.
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Analysis of Retinal Image Data to Support Glaucoma Diagnosis
Odstrčilík, Jan ; Kybic, Jan (referee) ; Matula,, Petr (referee) ; Kolář, Radim (advisor)
Fundus kamera je široce dostupné zobrazovací zařízení, které umožňuje relativně rychlé a nenákladné vyšetření zadního segmentu oka – sítnice. Z těchto důvodů se mnoho výzkumných pracovišť zaměřuje právě na vývoj automatických metod diagnostiky nemocí sítnice s využitím fundus fotografií. Tato dizertační práce analyzuje současný stav vědeckého poznání v oblasti diagnostiky glaukomu s využitím fundus kamery a navrhuje novou metodiku hodnocení vrstvy nervových vláken (VNV) na sítnici pomocí texturní analýzy. Spolu s touto metodikou je navržena metoda segmentace cévního řečiště sítnice, jakožto další hodnotný příspěvek k současnému stavu řešené problematiky. Segmentace cévního řečiště rovněž slouží jako nezbytný krok předcházející analýzu VNV. Vedle toho práce publikuje novou volně dostupnou databázi snímků sítnice se zlatými standardy pro účely hodnocení automatických metod segmentace cévního řečiště.
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Extraction of texture features aimed to detect glaucoma defects
Daněk, Daniel ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
The thesis deals with an automatic method of texture analysis using Markov random fields texture modeling. The main aim of this work is to find out relevant textural features, which can be used for appropriate classification of the degree of retinal nerve fiber layer loss. The model of Markovian statistic uses a circular symmetric neighborhood structure and a least square error estimation of the model's parameter. Obtained textural features were quantitatively evaluated using correlation analysis. The results show, that there is a significant correlation between proposed textural features and RNFL thickness measured by OCT. Thus, the features can potentially serve for glaucoma diagnosis.
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Analysis of Ophthalmological Images Aimed to Diagnosis of Glaucoma
Vodáková, Martina ; Kolář, Radim (referee) ; Odstrčilík, Jan (advisor)
Bachelor thesis is focused on fundamental texture analysis of high-resolution fundus images aimed to subjectively and quantitatively describe properties of texture formed by the retinal nerve fiber layer. An area of interest was predefined in the form of ten sectors on each fundus image. The correlation between results of subjective and quantitative evaluation of the texture was monitored in each sector. The results show that proposed fundamental texture features are closely related to the subjective textural properties obtained from visual appearance of the retinal nerve fiber layer. The last step compares results from fundamental texture analysis with quantitative measurement of the retinal nerve fiber layer thickness provided by Optical Coherence Tomography.
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Analysis of retinal nerve fiber layer in fundus images utilizing local binary patterns
Doležal, Petr ; Harabiš, Vratislav (referee) ; Odstrčilík, Jan (advisor)
This work describes LBP (Local Binary Pattern) method in its various forms as a tool for distinguishing images with and without texture. The first part of the essay looks into the retinal nerve fiber layer, loss of the nerve fiber and especially into possibilities of retinal images with help of the fundus camera and into properties of this way received data. Second part of the essay describes and explains the LBP method which uses local binary operators for description of texture by help of histograms. From this way brought force of histograms is possible to gain a complex of features. Due to different classification approaches can then determine if new samples were selected from an image loss of retinal nerve fiber layer (RNFL). This solves the next part of the essay. And then is evaluated the correlation of features of LBP histograms of these images with the thickness of the RNFL in the same place. The methods described in this essay have been tested on a set of images in Matlab program and received results show, that the method can be useful for the diagnosis of glaucoma diseases.
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Retinal nerve fiber layer measurement in patients with Alzheimer's disease
Kasl, Zdeněk ; Jirásková, Naďa (advisor) ; Pašta, Jiří (referee) ; Skorkovská, Šárka (referee)
Retinal nerve fiber layer measurement in patients with Alzheimer's disease The current ophthalmologist's possibilities in diagnostics of Alzheimer's disease Alzheimer disease (AD) is the most common cause of dementia syndrome and mild cognitive impairment. To enroll the disease most securely there are used so called biomarkers using evidence of changed brain metabolism by pozitron emission tomography (PET) and in cerebrospinal fluid or the brain's structure magnetic resonance imaging (MRI). These methods are expensive, organisationally and temporally challenging and burdening for the patients. According to that reasons we are still seeking for alternative attitudes suitable for early diagnosis. The evaluation of thickness of retinal nerve fiber layer (RNFL) which is well accessible to examination through optical aparatus of the eye could be one of the options. The aim of our work was to present current knowledges about Alzheimer's disease targeting relations of Alzheimer's disease and an ophthalmological finding. In the next part of this paper we introduce the retinal nerve fiber layer measurement by optical coherence tomography (OCT) as a potential diagnostics method by screening of patients with Alzheimer's disease and to present our results measured in our cohort of patients. The studied cohort...
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Retinal nerve fiber layer measurement in patients with Alzheimer's disease
Kasl, Zdeněk ; Jirásková, Naďa (advisor) ; Pašta, Jiří (referee) ; Skorkovská, Šárka (referee)
Retinal nerve fiber layer measurement in patients with Alzheimer's disease The current ophthalmologist's possibilities in diagnostics of Alzheimer's disease Alzheimer disease (AD) is the most common cause of dementia syndrome and mild cognitive impairment. To enroll the disease most securely there are used so called biomarkers using evidence of changed brain metabolism by pozitron emission tomography (PET) and in cerebrospinal fluid or the brain's structure magnetic resonance imaging (MRI). These methods are expensive, organisationally and temporally challenging and burdening for the patients. According to that reasons we are still seeking for alternative attitudes suitable for early diagnosis. The evaluation of thickness of retinal nerve fiber layer (RNFL) which is well accessible to examination through optical aparatus of the eye could be one of the options. The aim of our work was to present current knowledges about Alzheimer's disease targeting relations of Alzheimer's disease and an ophthalmological finding. In the next part of this paper we introduce the retinal nerve fiber layer measurement by optical coherence tomography (OCT) as a potential diagnostics method by screening of patients with Alzheimer's disease and to present our results measured in our cohort of patients. The studied cohort...
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Preprocessing of retinal images aimed at support diagnosis of glaucoma
Holásková, Anna ; Walek, Petr (referee) ; Odstrčilík, Jan (advisor)
Preprocessing of retinal images can serve as a first phase of the further image analysis or the first step preceding diagnosing of various eye diseases. The preprocessing thus represents methods of image adjustments that can improve visual characteristics of fundus images. These methods mainly include the removal of noise generated during data acquisition, contrast and brightness transformations, edge detection and thresholding. This work handles with the basic methods of image preprocessing and specific methods of preprocessing of retinal images. The preprocessing includes global illumination correction, high-pass and homomorphic filtering and adaptive enhancement of the images. Manual methods for fundus image preprocessing that are usually based on the doctor's experience can be used as well. Hence, a procedure for enhancement of retinal images using Adobe Photoshop is mentioned in this work too. Three methods for preprocessing of fundus images were selected and implemented in MATLAB programming software. These methods include homomorphic filtering, CLAHE (Contrast Limited Adaptive Histogram Equalization) and adaptive enhancement. Experimental program functions were created and tested on the available image data. Results of the selected methods are mentioned in the conclusion section. Instructions for use of implemented functions are in appendix.
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Analysis of retinal nerve fiber layer in fundus images utilizing local binary patterns
Doležal, Petr ; Harabiš, Vratislav (referee) ; Odstrčilík, Jan (advisor)
This work describes LBP (Local Binary Pattern) method in its various forms as a tool for distinguishing images with and without texture. The first part of the essay looks into the retinal nerve fiber layer, loss of the nerve fiber and especially into possibilities of retinal images with help of the fundus camera and into properties of this way received data. Second part of the essay describes and explains the LBP method which uses local binary operators for description of texture by help of histograms. From this way brought force of histograms is possible to gain a complex of features. Due to different classification approaches can then determine if new samples were selected from an image loss of retinal nerve fiber layer (RNFL). This solves the next part of the essay. And then is evaluated the correlation of features of LBP histograms of these images with the thickness of the RNFL in the same place. The methods described in this essay have been tested on a set of images in Matlab program and received results show, that the method can be useful for the diagnosis of glaucoma diseases.
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