Software Environment for Classification and Extraction of Retinal Lesions with using Artificial Intelligence Methods

Cílem této diplomové práce je vytvořit algoritmus v MATLABu a softwarové prostředí pro automatizovanou extrakci retinálních lézí z retinálních obrazových dat s využitím prvků umělé inteligence a následné testování výsledků navrhovaného algoritmu. Úvod práce pojednává o základních principech a přístrojích používaných pro zobrazování retinální oblasti. Nejdůležitější částí této práce je návrh hybridního algoritmu a jeho následné objektivní posouzení. Hybridní algoritmus kombinuje dva segmentační algoritmy a sestává ze tří hlavních částí. V první části je využit algoritmus pro segmentaci retinálních lézí, který však v mnoha případech chybně segmentuje i oblasti cévního řečiště. Z toho důvodu je v druhé části implementován algoritmus pro segmentaci cévního řečiště a oba výsledky segmentačních algoritmů se následně odečtou. Třetí částí hybridního algoritmu je postprocessing založený na extrakci příznaků. Vytvořený algoritmus byl testován na datasetu poskytnutém Oční klinikou Fakultní nemocnice v Ostravě. V závěru je vytvořeno graficko-uživatelské rozhraní v App Designeru.The aim of this diploma thesis is to create an algorithm in MATLAB and a software environment for automated extraction of retinal lesions from retinal image data using elements of artificial intelligence and subsequent testing of the results of the proposed algorithm. The introduction deals with the basic principles and devices used for imaging the retinal area. The most important part of this work is the design of a hybrid algorithm and its subsequent objective assessment. The hybrid algorithm combines two segmentation algorithms and consists of three main parts. In the first part, an algorithm for segmentation of retinal lesions is used, which, however, in many cases also incorrectly segments areas of the vascular riverbed. For this reason, an algorithm for vascular riverbed segmentation is implemented in the second part, and both results of the segmentation algorithms are subsequently subtracted. The third part of the hybrid algorithm is postprocessing based on feature extraction. The developed algorithm was tested on a dataset provided by the Eye Clinic of the University Hospital in Ostrava. Finally, a graphical user interface in the App Designer is created.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Deep learning of brain asymmetry digital biomarkers to support early diagnosis of cognitive decline and dementia

Early identification of degenerative processes in the human brain is essential for proper care and treatment. This may involve different instrumental diagnostic methods, including the most popular computer tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. These technologies provide detailed information about the shape, size, and function of the human brain. Structural and functional cerebral changes can be detected by computational algorithms and used to diagnose dementia and its stages (amnestic early mild cognitive impairment - EMCI, Alzheimer’s Disease - AD). They can help monitor the progress of the disease. Transformation shifts in the degree of asymmetry between the left and right hemispheres illustrate the initialization or development of a pathological process in the brain. In this vein, this study proposes a new digital biomarker for the diagnosis of early dementia based on the detection of image asymmetries and crosssectional comparison of NC (normal cognitively), EMCI and AD subjects. Features of brain asymmetries extracted from MRI of the ADNI and OASIS databases are used to analyze structural brain changes and machine learning classification of the pathology. The experimental part of the study includes results of supervised machine learning algorithms and transfer learning architectures of convolutional neural networks for distinguishing between cognitively normal subjects and patients with early or progressive dementia. The proposed pipeline offers a low-cost imaging biomarker for the classification of dementia. It can be potentially helpful to other brain degenerative disorders accompanied by changes in brain asymmetries

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

MATLAB

A well-known statement says that the PID controller is the "bread and butter" of the control engineer. This is indeed true, from a scientific standpoint. However, nowadays, in the era of computer science, when the paper and pencil have been replaced by the keyboard and the display of computers, one may equally say that MATLAB is the "bread" in the above statement. MATLAB has became a de facto tool for the modern system engineer. This book is written for both engineering students, as well as for practicing engineers. The wide range of applications in which MATLAB is the working framework, shows that it is a powerful, comprehensive and easy-to-use environment for performing technical computations. The book includes various excellent applications in which MATLAB is employed: from pure algebraic computations to data acquisition in real-life experiments, from control strategies to image processing algorithms, from graphical user interface design for educational purposes to Simulink embedded systems