A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

Ischemic stroke is a severe neuro disorder typically characterized by a block inside a blood vessel supplying blood to the brain. It remains the third leading cause for death, after heart attack and cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were the vital major imaging techniques used for diagnosing this disorder. While the CT imaging can be used at the primary stage, MRI proves to be a standard aid for progressive diagnostic planning in the treatment of stroke injuries. Developing a fully automatic approach for lesion segmentation is a challenging issue due to the complex nature of the lesions structures. This research basically aims at examining the properties of such complex structures. It analyses the characteristics of the normal brain tissues and abnormal lesion structures using a three-level wavelet decomposition procedure. Four different wavelet functions namely daubechies, symlet, coiflet and de-meyer were applied to the different datasets and the resulting observations were examined based on their feature statistics obtained. Experiments indicate the feature statistics obtained from daubechies and de-meyer wavelets were able to clearly distinguish between the typical brain tissues and abnormal lesion structures

Contributions in image and video coding

Orientador: Max Henrique Machado CostaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A comunidade de codificação de imagens e vídeo vem também trabalhando em inovações que vão além das tradicionais técnicas de codificação de imagens e vídeo. Este trabalho é um conjunto de contribuições a vários tópicos que têm recebido crescente interesse de pesquisadores na comunidade, nominalmente, codificação escalável, codificação de baixa complexidade para dispositivos móveis, codificação de vídeo de múltiplas vistas e codificação adaptativa em tempo real. A primeira contribuição estuda o desempenho de três transformadas 3-D rápidas por blocos em um codificador de vídeo de baixa complexidade. O codificador recebeu o nome de Fast Embedded Video Codec (FEVC). Novos métodos de implementação e ordens de varredura são propostos para as transformadas. Os coeficiente 3-D são codificados por planos de bits pelos codificadores de entropia, produzindo um fluxo de bits (bitstream) de saída totalmente embutida. Todas as implementações são feitas usando arquitetura com aritmética inteira de 16 bits. Somente adições e deslocamentos de bits são necessários, o que reduz a complexidade computacional. Mesmo com essas restrições, um bom desempenho em termos de taxa de bits versus distorção pôde ser obtido e os tempos de codificação são significativamente menores (em torno de 160 vezes) quando comparados ao padrão H.264/AVC. A segunda contribuição é a otimização de uma recente abordagem proposta para codificação de vídeo de múltiplas vistas em aplicações de video-conferência e outras aplicações do tipo "unicast" similares. O cenário alvo nessa abordagem é fornecer vídeo com percepção real em 3-D e ponto de vista livre a boas taxas de compressão. Para atingir tal objetivo, pesos são atribuídos a cada vista e mapeados em parâmetros de quantização. Neste trabalho, o mapeamento ad-hoc anteriormente proposto entre pesos e parâmetros de quantização é mostrado ser quase-ótimo para uma fonte Gaussiana e um mapeamento ótimo é derivado para fonte típicas de vídeo. A terceira contribuição explora várias estratégias para varredura adaptativa dos coeficientes da transformada no padrão JPEG XR. A ordem de varredura original, global e adaptativa do JPEG XR é comparada com os métodos de varredura localizados e híbridos propostos neste trabalho. Essas novas ordens não requerem mudanças nem nos outros estágios de codificação e decodificação, nem na definição da bitstream A quarta e última contribuição propõe uma transformada por blocos dependente do sinal. As transformadas hierárquicas usualmente exploram a informação residual entre os níveis no estágio da codificação de entropia, mas não no estágio da transformada. A transformada proposta neste trabalho é uma técnica de compactação de energia que também explora as similaridades estruturais entre os níveis de resolução. A idéia central da técnica é incluir na transformada hierárquica um número de funções de base adaptativas derivadas da resolução menor do sinal. Um codificador de imagens completo foi desenvolvido para medir o desempenho da nova transformada e os resultados obtidos são discutidos neste trabalhoAbstract: The image and video coding community has often been working on new advances that go beyond traditional image and video architectures. This work is a set of contributions to various topics that have received increasing attention from researchers in the community, namely, scalable coding, low-complexity coding for portable devices, multiview video coding and run-time adaptive coding. The first contribution studies the performance of three fast block-based 3-D transforms in a low complexity video codec. The codec has received the name Fast Embedded Video Codec (FEVC). New implementation methods and scanning orders are proposed for the transforms. The 3-D coefficients are encoded bit-plane by bit-plane by entropy coders, producing a fully embedded output bitstream. All implementation is performed using 16-bit integer arithmetic. Only additions and bit shifts are necessary, thus lowering computational complexity. Even with these constraints, reasonable rate versus distortion performance can be achieved and the encoding time is significantly smaller (around 160 times) when compared to the H.264/AVC standard. The second contribution is the optimization of a recent approach proposed for multiview video coding in videoconferencing applications or other similar unicast-like applications. The target scenario in this approach is providing realistic 3-D video with free viewpoint video at good compression rates. To achieve such an objective, weights are computed for each view and mapped into quantization parameters. In this work, the previously proposed ad-hoc mapping between weights and quantization parameters is shown to be quasi-optimum for a Gaussian source and an optimum mapping is derived for a typical video source. The third contribution exploits several strategies for adaptive scanning of transform coefficients in the JPEG XR standard. The original global adaptive scanning order applied in JPEG XR is compared with the localized and hybrid scanning methods proposed in this work. These new orders do not require changes in either the other coding and decoding stages or in the bitstream definition. The fourth and last contribution proposes an hierarchical signal dependent block-based transform. Hierarchical transforms usually exploit the residual cross-level information at the entropy coding step, but not at the transform step. The transform proposed in this work is an energy compaction technique that can also exploit these cross-resolution-level structural similarities. The core idea of the technique is to include in the hierarchical transform a number of adaptive basis functions derived from the lower resolution of the signal. A full image codec is developed in order to measure the performance of the new transform and the obtained results are discussed in this workDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétric

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Preserving data integrity of encoded medical images: the LAR compression framework

International audienceThrough the development of medical imaging systems and their integration into a complete information system, the need for advanced joint coding and network services becomes predominant. PACS (Picture Archiving and Communication System) aims to acquire, store and compress, retrieve, present and distribute medical images. These systems have to be accessible via the Internet or wireless channels. Thus protection processes against transmission errors have to be added to get a powerful joint source-channel coding tool. Moreover, these sensitive data require confidentiality and privacy for both archiving and transmission purposes, leading to use cryptography and data embedding solutions. This chapter introduces data integrity protection and developed dedicated tools of content protection and secure bitstream transmission for medical encoded image purposes. In particular, the LAR image coding method is defined together with advanced securization services

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Mining complex data in highly streaming environments

Data is growing at a rapid rate because of advanced hardware and software technologies and platforms such as e-health systems, sensor networks, and social media. One of the challenging problems is storing, processing and transferring this big data in an efficient and effective way. One solution to tackle these challenges is to construct synopsis by means of data summarization techniques. Motivated by the fact that without summarization, processing, analyzing and communicating this vast amount of data is inefficient, this thesis introduces new summarization frameworks with the main goals of reducing communication costs and accelerating data mining processes in different application scenarios. Specifically, we study the following big data summarizaion techniques:(i) dimensionality reduction;(ii)clustering,and(iii)histogram, considering their importance and wide use in various areas and domains. In our work, we propose three different frameworks using these summarization techniques to cover three different aspects of big data:"Volume","Velocity"and"Variety" in centralized and decentralized platforms. We use dimensionality reduction techniques for summarizing large 2D-arrays, clustering and histograms for processing multiple data streams. With respect to the importance and rapid growth of emerging e-health applications such as tele-radiology and tele-medicine that require fast, low cost, and often lossless access to massive amounts of medical images and data over band limited channels,our first framework attempts to summarize streams of large volume medical images (e.g. X-rays) for the purpose of compression. Significant amounts of correlation and redundancy exist across different medical images. These can be extracted and used as a data summary to achieve better compression, and consequently less storage and less communication overheads on the network. We propose a novel memory-assisted compression framework as a learning-based universal coding, which can be used to complement any existing algorithm to further eliminate redundancies/similarities across images. This approach is motivated by the fact that, often in medical applications, massive amounts of correlated images from the same family are available as training data for learning the dependencies and deriving appropriate reference or synopses models. The models can then be used for compression of any new image from the same family. In particular, dimensionality reduction techniques such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) are applied on a set of images from training data to form the required reference models. The proposed memory-assisted compression allows each image to be processed independently of other images, and hence allows individual image access and transmission. In the second part of our work,we investigate the problem of summarizing distributed multidimensional data streams using clustering. We devise a distributed clustering framework, DistClusTree, that extends the centralized ClusTree approach. The main difficulty in distributed clustering is balancing communication costs and clustering quality. We tackle this in DistClusTree through combining spatial index summaries and online tracking for efficient local and global incremental clustering. We demonstrate through extensive experiments the efficacy of the framework in terms of communication costs and approximate clustering quality. In the last part, we use a multidimensional index structure to merge distributed summaries in the form of a centralized histogram as another widely used summarization technique with the application in approximate range query answering. In this thesis, we propose the index-based Distributed Mergeable Summaries (iDMS) framework based on kd-trees that addresses these challenges with data generative models of Gaussian mixture models (GMMs) and a Generative Adversarial Network (GAN). iDMS maintains a global approximate kd-tree at a central site via GMMs or GANs upon new arrivals of streaming data at local sites. Experimental results validate the effectiveness and efficiency of iDMS against baseline distributed settings in terms of approximation error and communication costs