Modeling of the Retinal Vascular System for Image Extraction and Standardization of Vascular Tortuosity

Diplomová práce se komplexně zabývá analýzou a modelováním retinálního cévního systému. Hlavním námětem práce je design a realizace plně automatického segmentačního modelu, umožňující extrakci oblasti cévního systému v rámci binárního modelu, kde ostatní retinální struktury jsou potlačeny. Důležitou vlastností modelu je senzitivita a robustnost, aby bylo možno deklarovat efektivitu segmentace v prostředí, disponující horšími obrazovými parametry. Z tohoto důvodu je navržený model rovněž testován pro data, kde cévní systém je vizualizován pod špatným kontrastem. Součástí analýzy je komparativní testování navrženého modelu vůči vybraným segmentačním metodám na základě objektivních měřítek. Navržený segmentační model umožňuje extrakci příznaků, hodnotící globální míru zakřivení příslušného cévního systému. Tato procedura je klíčová pro klasifikaci patologické tortuozity, která je rovněž námětem této práce. Navržený model je vybaven registrační procedurou, umožňující kompenzovat geometrické diference při opakovaném snímání retiny. Navržený model byl testován a verifikován na datasetu ze systému RetCam 3, čítající 44 záznamů.This diploma thesis deals with complex analysis and modeling of the retinal vascular system. The aim of the thesis is the design and implementation of a fully automated segmentation model, allowing extraction of the blood system area within the binary model, where other retinal structures are suppressed. An important feature of the model is sensitivity and robustness to declare the effiency of segmentation in a environment with worse image parameters. For this reason, the designed model is also tested for data where the vascular system is visualized under low contrast. Part of the analysis is the comparitive testing of the designed model against selected segmentation methods based on objective criteria. Designed segmentation model allows the extraction of symptoms that evaluate the global degree of curvature of the vascular system. This procedure is key for the classification of pathological tortuosity, which i salso the subject of this work. The designed model is equipped with a registration procedure that allows to compensate geometric differences during repeated retinal scanning. The designed model was tested and verified on dataset from system RetCam 3, containing 44 images.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

The Algorithm for Automatic Detection and Tortuosity Assessment of Retinal Blood Vessels

Import 03/11/2016Cílem této bakalářské práce je vytvořit algoritmus pro automatizovanou detekci cévního řečiště a algoritmus pro výpočet a hodnocení tortuozity. Klíčovými místy této práce jsou návrh a realizace již zmíněných algoritmů a návrh algoritmu pro výpočet objemu cév. Vytvořené algoritmy byly testovány na datasetu snímků sítnice nedonošených dětí, který byl poskytnut v anonymní podobě Centrem pro děti s vadami zraku Oční kliniky Fakultní nemocnice v Ostravě. Výstupem této práce je grafické uživatelské rozhraní vytvořené v MATLABU.TThe aim of this thesis is created algorithm for automatic detection of blood vessels and algorithm for calculation and evaluation of tortuosity. The key area of this aim are design and implementation the previous algorithms and design of algorithm for calculation volume of blood vessels. The algorithms were tested at the dataset of retinal image of the prematurity which is provided in anonymous form by Ophthalmologic clinic in Ostrava. Output of the thesis is graphic user interface. It was created at MATLAB.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Review: Development and technical design of tangible user interfaces in wide-field areas of application

A tangible user interface or TUI connects physical objects and digital interfaces. It is more interactive and interesting for users than a classic graphic user interface. This article presents a descriptive overview of TUI's real-world applications sorted into ten main application areas-teaching of traditional subjects, medicine and psychology, programming, database development, music and arts, modeling of 3D objects, modeling in architecture, literature and storytelling, adjustable TUI solutions, and commercial TUI smart toys. The paper focuses on TUI's technical solutions and a description of technical constructions that influences the applicability of TUIs in the real world. Based on the review, the technical concept was divided into two main approaches: the sensory technical concept and technology based on a computer vision algorithm. The sensory technical concept is processed to use wireless technology, sensors, and feedback possibilities in TUI applications. The image processing approach is processed to a marker and markerless approach for object recognition, the use of cameras, and the use of computer vision platforms for TUI applications.Web of Science2113art. no. 425

Detection and Segmentation of Retinal Lesions in Retcam 3 Images Based on Active Contours Driven by Statistical Local Features

Clinical retinal image analysis is an import aspect of clinical diagnosis in ophthalmology. Retinopathy of Prematurity (ROP) represents one of the most severe retinal disorders in prematurely born infants. One of the ROP clinical signs is the presence of retinal lesions endangering the vision system. Unfortunately, the stage and progress of these findings is often only subjectively estimated. A procedure such as this is undoubtedly linked to subjective inaccuracies depending on the experience of the ophthalmologist. In our study, a fully autonomous segmentation algorithm to model retinal lesions found using RetCam 3 is proposed. The proposed method used a combination of retinal image preprocessing and active contours for retinal lesion segmentation. Based on this procedure, a binary model of retinal lesions that allowed retinal lesions to be classified from a retinal image background was obtained. Another important aspect of the model was feature extraction. These features reliably and automatically described the development stage of an individual lesion. A complex procedure such as this has significant implications for ophthalmic clinical practice in substituting manual clinical procedures and improving the accuracy of routine clinical decisions

Recent techniques and trends for retinal blood vessel extraction and tortuosity evaluation: A comprehensive review

Retinal blood vessel segmentation plays an important part in the early diagnosis and treatment of eye disease. It is a tool for ophthalmologists. Many diseases can be identified by examining manifestations and images of blood vessels, including diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinopathy due to hypertension, glaucoma and others. Early detection allows physicians to provide patients with effective treatment, while in the opposite case, the late detection of retinal disease can ultimately lead to blindness. One of the indices when examining the retina is an evaluation of blood vessels based on tortuosity, i.e. the degree of curvature of blood vessels. This article presents a comprehensive overview of all segmentation techniques for retinal blood vessel extraction from images taken with a fundus camera in adults and older children or with a RetCam fundus camera in new-borns and younger children over the last 10 years. An integral part of this review is a comprehensive overview with information on all available public and private databases with retinal images. The review includes an evaluation of segmentation techniques based on objectivization parameters, including information on all objectivization parameters used in this article. As already mentioned, the degree of curvature of retinal blood vessels is used to classify severity of blood vessels tortuosity. There is no uniform metric for determining tortuosity, but this review presents a comprehensive overview of all metrics and calculations used to determine the degree of tortuosity of retinal blood vessels.Web of Science819781619778

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images.

Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images

Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.Web of Science177art. no. e027074