Image database system for glaucoma diagnosis support

Tato práce popisuje přehled standardních a pokročilých metod používaných k diagnose glaukomu v ranném stádiu. Na základě teoretických poznatků je implementován internetově orientovaný informační systém pro oční lékaře, který má tři hlavní cíle. Prvním cílem je možnost sdílení osobních dat konkrétního pacienta bez nutnosti posílat tato data internetem. Druhým cílem je vytvořit účet pacienta založený na kompletním očním vyšetření. Posledním cílem je aplikovat algoritmus pro registraci intenzitního a barevného fundus obrazu a na jeho základě vytvořit internetově orientovanou tři-dimenzionální vizualizaci optického disku. Tato práce je součásti DAAD spolupráce mezi Ústavem Biomedicínského Inženýrství, Vysokého Učení Technického v Brně, Oční klinikou v Erlangenu a Ústavem Informačních Technologií, Friedrich-Alexander University, Erlangen-Nurnberg.This master thesis describes a conception of standard and advanced eye examination methods used for glaucoma diagnosis in its early stage. According to the theoretical knowledge, a web based information system for ophthalmologists with three main aims is implemented. The first aim is the possibility to share medical data of a concrete patient without sending his personal data through the Internet. The second aim is to create a patient account based on a complete eye examination procedure. The last aim is to improve the HRT diagnostic method with an image registration algorithm for the fundus and intensity images and create an optic nerve head web based 3D visualization. This master thesis is a part of project based on DAAD co-operation between Department of Biomedical Engineering, Brno University of Technology, Eye Clinic in Erlangen and Department of Computer Science, Friedrich-Alexander University, Erlangen-Nurnberg.

A Hybrid Similarity Measure Framework for Multimodal Medical Image Registration

Medical imaging is widely used today to facilitate both disease diagnosis and treatment planning practice, with a key prerequisite being the systematic process of medical image registration (MIR) to align either mono or multimodal images of different anatomical parts of the human body. MIR utilises a similarity measure (SM) to quantify the level of spatial alignment and is particularly demanding due to the presence of inherent modality characteristics like intensity non-uniformities (INU) in magnetic resonance images and large homogeneous non-vascular regions in retinal images. While various intensity and feature-based SMs exist for MIR, mutual information (MI) has become established because of its computational efficiency and ability to register multimodal images. It is however, very sensitive to interpolation artefacts in the presence of INU with noise and can be compromised when overlapping areas are small. Recently MI-based hybrid variants which combine regional features with intensity have emerged, though these incur high dimensionality and large computational overheads. To address these challenges and secure accurate, efficient and robust registration of images containing high INU, noise and large homogeneous regions, this thesis presents a new hybrid SM framework for 2D multimodal rigid MIR. The framework consistently provides superior quantitative and qualitative performance, while offering a uniquely flexible design trade-off between registration accuracy and computational time. It makes three significant technical contributions to the field: i) An expectation maximisation-based principal component analysis with mutual information (EMPCA-MI) framework incorporating neighbourhood feature information; ii) Two innovative enhancements to reduce information redundancy and improve MI computational efficiency; and iii) an adaptive algorithm to select the most significant principal components for feature selection. The thesis findings conclusively confirm the hybrid SM framework offers an accurate and robust 2D registration solution for challenging multimodal medical imaging datasets, while its inherent flexibility means it can also be extended to the 3D registration domain

Medical image registration using unsupervised deep neural network: A scoping literature review

In medicine, image registration is vital in image-guided interventions and other clinical applications. However, it is a difficult subject to be addressed which by the advent of machine learning, there have been considerable progress in algorithmic performance has recently been achieved for medical image registration in this area. The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy, playing a key role in countering tumors during the operation. The current study presents a comprehensive scoping review on the state-of-the-art literature of medical image registration studies based on unsupervised deep neural networks is conducted, encompassing all the related studies published in this field to this date. Here, we have tried to summarize the latest developments and applications of unsupervised deep learning-based registration methods in the medical field. Fundamental and main concepts, techniques, statistical analysis from different viewpoints, novelties, and future directions are elaborately discussed and conveyed in the current comprehensive scoping review. Besides, this review hopes to help those active readers, who are riveted by this field, achieve deep insight into this exciting field

Multimodal retinal image registration using a fast principal component analysis hybrid-based similarity measure

Multimodal retinal images (RI) are extensively used for analysing various eye diseases and conditions such as myopia and diabetic retinopathy. The incorporation of either two or more RI modalities provides complementary structure information in the presence of non-uniform illumination and low-contrast homogeneous regions. It also presents significant challenges for retinal image registration (RIR). This paper investigates how the Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) algorithm can effectively achieve multimodal RIR. This iterative hybrid-based similarity measure combines spatial features with mutual information to provide enhanced registration without recourse to either segmentation or feature extraction. Experimental results for clinical multimodal RI datasets comprising colour fundus and scanning laser ophthalmoscope images confirm EMPCA-MI is able to consistently afford superior numerical and qualitative registration performance compared with existing RIR techniques, such as the bifurcation structures method

Retinal Fundus Image Registration via Vascular Structure Graph Matching

Motivated by the observation that a retinal fundus image may contain some unique geometric structures within its vascular trees which can be utilized for feature matching, in this paper, we proposed a graph-based registration framework called GM-ICP to align pairwise retinal images. First, the retinal vessels are automatically detected and represented as vascular structure graphs. A graph matching is then performed to find global correspondences between vascular bifurcations. Finally, a revised ICP algorithm incorporating with quadratic transformation model is used at fine level to register vessel shape models. In order to eliminate the incorrect matches from global correspondence set obtained via graph matching, we proposed a structure-based sample consensus (STRUCT-SAC) algorithm. The advantages of our approach are threefold: (1) global optimum solution can be achieved with graph matching; (2) our method is invariant to linear geometric transformations; and (3) heavy local feature descriptors are not required. The effectiveness of our method is demonstrated by the experiments with 48 pairs retinal images collected from clinical patients

Motion Correction in Optical Coherence Tomography for Multi-modality Retinal Image Registration

Optical coherence tomography (OCT) is a recently developed non-invasive imaging modality, which is often used in ophthalmology. Because of the sequential scanning in form of A-scans, OCT suffers from the inevitable eye movement. This often leads to mis-alignment especially among consecutive B-scans, which affects the analysis and processing of the data such as the registration of the OCT en face image to color fundus image. In this paper, we propose a novel method to correct the mis-alignment among consecutive B-scans to improve the accuracy in multi-modality retinal image registration. In the method, we propose to compute decorrelation from overlapping B-scans and to detect the eye movement. Then, the B-scans with eye movement will be re-aligned to its precedent scans while the rest of B-scans without eye movement are untouched. Our experiments results show that the proposed method improves the accuracy and success rate in the registration to color fundus images

Ophthalmologic Image Registration Based on Shape-Context: Application to Fundus Autofluorescence (FAF) Images

Online access to subscriber only at http://www.actapress.com/Content_Of_Proceeding.aspx?ProceedingID=494International audienceA novel registration algorithm, which was developed in order to facilitate ophthalmologic image processing, is presented in this paper. It has been evaluated on FAF images, which present low Si gnal/Noise Ratio (SNR) and variations in dynamic grayscale range. These characteristics complicate the registration process and cause a failure to area-based registration techniques [1, 2] . Our method is based on shape-context theory [3] . In the first step, images are enhanced by Gaussian model based histog ram modification. Features are extracted in the next step by morphological operators, which are used to detect an approximation of vascular tree from both reference and floating images. Simplified medial axis of vessels is then calculated. From each image, a set of control points called Bifurcation Points (BPs) is extracted from the medial axis through a new fast algorithm. Radial histogram is formed for each BP using the medial axis. The Chi2 distance is measured between two sets of BPs based on radial histogram. Hungarian algorithm is applied to assign the correspondence among BPs from reference and floating images. The algorithmic robustness is evaluated by mutual information criteria between manual registration considered as Ground Truth and automatic one

Fusion based analysis of ophthalmologic image data

summary:The paper presents an overview of image analysis activities of the Brno DAR group in the medical application area of retinal imaging. Particularly, illumination correction and SNR enhancement by registered averaging as preprocessing steps are briefly described; further mono- and multimodal registration methods developed for specific types of ophthalmological images, and methods for segmentation of optical disc, retinal vessel tree and autofluorescence areas are presented. Finally, the designed methods for neural fibre layer detection and evaluation on retinal images, utilising different combined texture analysis approaches and several types of classifiers, are shown. The results in all the areas are shortly commented on at the respective sections. In order to emphasise methodological aspects, the methods and results are ordered according to consequential phases of processing rather then divided according to individual medical applications

Deep Learning Techniques for Automated Analysis and Processing of High Resolution Medical Imaging

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] Medical imaging plays a prominent role in modern clinical practice for numerous medical specialties. For instance, in ophthalmology, different imaging techniques are commonly used to visualize and study the eye fundus. In this context, automated image analysis methods are key towards facilitating the early diagnosis and adequate treatment of several diseases. Nowadays, deep learning algorithms have already demonstrated a remarkable performance for different image analysis tasks. However, these approaches typically require large amounts of annotated data for the training of deep neural networks. This complicates the adoption of deep learning approaches, especially in areas where large scale annotated datasets are harder to obtain, such as in medical imaging. This thesis aims to explore novel approaches for the automated analysis of medical images, particularly in ophthalmology. In this regard, the main focus is on the development of novel deep learning-based approaches that do not require large amounts of annotated training data and can be applied to high resolution images. For that purpose, we have presented a novel paradigm that allows to take advantage of unlabeled complementary image modalities for the training of deep neural networks. Additionally, we have also developed novel approaches for the detailed analysis of eye fundus images. In that regard, this thesis explores the analysis of relevant retinal structures as well as the diagnosis of different retinal diseases. In general, the developed algorithms provide satisfactory results for the analysis of the eye fundus, even when limited annotated training data is available.[Resumen] Las técnicas de imagen tienen un papel destacado en la práctica clínica moderna de numerosas especialidades médicas. Por ejemplo, en oftalmología es común el uso de diferentes técnicas de imagen para visualizar y estudiar el fondo de ojo. En este contexto, los métodos automáticos de análisis de imagen son clave para facilitar el diagnóstico precoz y el tratamiento adecuado de diversas enfermedades. En la actualidad, los algoritmos de aprendizaje profundo ya han demostrado un notable rendimiento en diferentes tareas de análisis de imagen. Sin embargo, estos métodos suelen necesitar grandes cantidades de datos etiquetados para el entrenamiento de las redes neuronales profundas. Esto complica la adopción de los métodos de aprendizaje profundo, especialmente en áreas donde los conjuntos masivos de datos etiquetados son más difíciles de obtener, como es el caso de la imagen médica. Esta tesis tiene como objetivo explorar nuevos métodos para el análisis automático de imagen médica, concretamente en oftalmología. En este sentido, el foco principal es el desarrollo de nuevos métodos basados en aprendizaje profundo que no requieran grandes cantidades de datos etiquetados para el entrenamiento y puedan aplicarse a imágenes de alta resolución. Para ello, hemos presentado un nuevo paradigma que permite aprovechar modalidades de imagen complementarias no etiquetadas para el entrenamiento de redes neuronales profundas. Además, también hemos desarrollado nuevos métodos para el análisis en detalle de las imágenes del fondo de ojo. En este sentido, esta tesis explora el análisis de estructuras retinianas relevantes, así como el diagnóstico de diferentes enfermedades de la retina. En general, los algoritmos desarrollados proporcionan resultados satisfactorios para el análisis de las imágenes de fondo de ojo, incluso cuando la disponibilidad de datos de entrenamiento etiquetados es limitada.[Resumo] As técnicas de imaxe teñen un papel destacado na práctica clínica moderna de numerosas especialidades médicas. Por exemplo, en oftalmoloxía é común o uso de diferentes técnicas de imaxe para visualizar e estudar o fondo de ollo. Neste contexto, os métodos automáticos de análises de imaxe son clave para facilitar o diagn ostico precoz e o tratamento adecuado de diversas enfermidades. Na actualidade, os algoritmos de aprendizaxe profunda xa demostraron un notable rendemento en diferentes tarefas de análises de imaxe. Con todo, estes métodos adoitan necesitar grandes cantidades de datos etiquetos para o adestramento das redes neuronais profundas. Isto complica a adopción dos métodos de aprendizaxe profunda, especialmente en áreas onde os conxuntos masivos de datos etiquetados son máis difíciles de obter, como é o caso da imaxe médica. Esta tese ten como obxectivo explorar novos métodos para a análise automática de imaxe médica, concretamente en oftalmoloxía. Neste sentido, o foco principal é o desenvolvemento de novos métodos baseados en aprendizaxe profunda que non requiran grandes cantidades de datos etiquetados para o adestramento e poidan aplicarse a imaxes de alta resolución. Para iso, presentamos un novo paradigma que permite aproveitar modalidades de imaxe complementarias non etiquetadas para o adestramento de redes neuronais profundas. Ademais, tamén desenvolvemos novos métodos para a análise en detalle das imaxes do fondo de ollo. Neste sentido, esta tese explora a análise de estruturas retinianas relevantes, así como o diagnóstico de diferentes enfermidades da retina. En xeral, os algoritmos desenvolvidos proporcionan resultados satisfactorios para a análise das imaxes de fondo de ollo, mesmo cando a dispoñibilidade de datos de adestramento etiquetados é limitada