Computational Modeling for Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, and Grading

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology (DP) images. Due to uncontrolled growth and infiltration into the surrounding tissues, the tumor regions appear with a significant texture variation in the static MRI volume and also in the longitudinal imaging study. Consequently, this study developed computational models using novel texture features to segment abnormal brain tissues (tumor, and stroke lesions), tracking the change of tumor volume in longitudinal images, and tumor grading in MR images. Manual delineation and analysis of these abnormal tissues in large scale is tedious, error-prone, and often suffers from inter-observer variability. Therefore, efficient computational models for robust segmentation of different abnormal tissues is required to support the diagnosis and analysis processes. In this study, brain tissues are characterized with novel computational modeling of multi-fractal texture features for multi-class brain tumor tissue segmentation (BTS) and extend the method for ischemic stroke lesions in MRI. The robustness of the proposed segmentation methods is evaluated using a huge amount of private and public domain clinical data that offers competitive performance when compared with that of the state-of-the-art methods. Further, I analyze the dynamic texture behavior of tumor volume in longitudinal imaging and develop post-processing frame-work using three-dimensional (3D) texture features. These post-processing methods are shown to reduce the false positives in the BTS results and improve the overall segmentation result in longitudinal imaging. Furthermore, using this improved segmentation results the change of tumor volume has been quantified in three types such as stable, progress, and shrinkage as observed by the volumetric changes of different tumor tissues in longitudinal images. This study also investigates a novel non-invasive glioma grading, for the first time in literature, that uses structural MRI only. Such non-invasive glioma grading may be useful before an invasive biopsy is recommended. This study further developed an automatic glioma grading scheme using the invasive cell nuclei morphology in DP images for cross-validation with the same patients. In summary, the texture-based computational models proposed in this study are expected to facilitate the clinical management of patients with the brain tumors and strokes by automating large scale imaging data analysis, reducing human error, inter-observer variability, and producing repeatable brain tumor quantitation and grading

Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética

A esclerose múltipla (EM) é o distúrbio neurológico mais comum diagnosticado em jovens adultos com causas inexplicáveis e grandes repercussões na vida dos pacientes, incitando os investigadores na procura ativa de respostas. Embora a doença não possa ser curada ou prevenida, neste momento, os tratamentos disponíveis permitem apenas reduzir a gravidade da mesma e retardar a sua progressão. Torna-se cada vez mais necessário recorrer a técnicas de imagiologia e de processamento e análise de imagem, para ajudar os médicos a realizar um diagnóstico precoce e iniciar o tratamento adequado a fim de proporcionar uma melhor qualidade de vida ao paciente. Várias abordagens baseadas em segmentação automática de lesões de esclerose múltipla tem sido amplamente investigadas nos últimos anos com esse objetivo.Para o desenvolvimento deste projeto, procurou-se por um lado, o reconhecimento das etapas necessárias para implementação e otimização de uma metodologia de processamento e análise de imagem para segmentação automática de lesões de EM, e por outro, a exploração de técnicas de pré-processamento, segmentação e classificação para caracterização objetiva e quantitativa das lesões. Neste trabalho serão ainda abordados conceitos fundamentais sobre a doença de esclerose múltipla e da técnica de ressonância magnética (RM), bem como o estudo bibliográfico de algumas das metodologias atualmente existentes.A metodologia desenvolvida nesta Dissertação teve como base a implementação de diversos algoritmos de pré-processamento para suavização e remoção de ruído, remoção de tecidos não-cerebrais, correção de contraste e normalização de intensidade das imagens. Para segmentação de lesões foi aplicado o estudo de redes neuronais, uma abordagem bastante promissora e atual para o problema proposto, e para classificação foram extraídas e analisadas algumas características das lesões através da sua forma e tamanho. Pretende-se que esta nova metodologia seja flexível e permita o ensaio e a análise dos resultados.Os resultados obtidos demonstram que as técnicas de pré-processamento se revelam essenciais para as etapas subsequentes permitindo uma melhor qualidade da imagem. A segmentação de lesões através do uso de redes neuronais revelou-se apropriada tal como comprovado pelas métricas analisadas, com índice de similaridade estrutural muito próximo de 1, taxa de erro absoluto médio de 3,8% e coeficiente de Dice de 0,58. Por fim, pelas várias aplicações práticas realizadas, foi possível demonstrar a utilidade e adequação das técnicas de processamento e análise de imagem no estudo e deteção de lesões de esclerose múltipla através de imagens de RM.Multiple sclerosis is the most commonly diagnosed neurological disorder in young adults with unexplained causes and major repercussions in the lives of patients, urging researchers to actively search for answers. Although the disease cannot be cured or prevented, the available treatments nowadays reduce its severity and delay its progression. It is becoming increasingly necessary to use imaging techniques and also image processing and analysis techniques, to help doctors perform an early diagnosis and start appropriate treatment in order to provide a better quality of life for the patient. Several approaches based on automatic segmentation of multiple sclerosis lesions have been extensively investigated in recent years for this purpose.This project was developed, firstly, with the recognition of the steps necessary to implement and optimize an image processing and analysis methodology for automatic segmentation of MS lesions, and secondly, by the exploration of pre-processing, segmentation and classification techniques for objective and quantitative characterization of the lesions. This work will also be discussed basic concepts of multiple sclerosis disease and magnetic resonance imaging (MRI), as well as the bibliographical study of some of the currently existing methodologies.The methodology developed in this dissertation was based on the implementation of several pre-processing algorithms for noise smoothing and removal, non-cerebral tissue removal, contrast correction and normalization of images intensity. For lesion segmentation was applied to the study of neural networks, a very promising and current approach to the proposed problem, and to classify were extracted and analyzed some characteristics of the lesions through shape and size. It is intended that this new methodology is flexible and allow the testing and analysis of the results.The results obtained demonstrate that pre-processing techniques are essential to the subsequent steps allowing better image quality. Segmentation of lesions through the use of neural networks proved to be appropriate for this study as shown by the metrics analyzed, with a structural similarity index very close to 1, mean absolute error rate of 3.8% and Dice coefficient of 0.58. Finally, the various practical applications performed was possible to demonstrate the usefulness and adequacy of image processing and analysis techniques in the study and detection of multiple sclerosis lesions through MR images