Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

Computational Strategies for Object Recognition

This article reviews the available methods forautomated identification of objects in digital images. The techniques are classified into groups according to the nature of the computational strategy used. Four classes are proposed: (1) the s~mplest strategies, which work on data appropriate for feature vector classification, (2) methods that match models to symbolic data structures for situations involving reliable data and complex models, (3) approaches that fit models to the photometry and are appropriate for noisy data and simple models, and (4) combinations of these strategies, which must be adopted in complex situations Representative examples of various methods are summarized, and the classes of strategies are evaluated with respect to their appropriateness for particular applications

Satellite Image Compression Using Wavelet

Image data is a combination of information and redundancies, the information is part of the data be protected because it contains the meaning and designation data. Meanwhile, the redundancies are part of data that can be reduced, compressed, or eliminated. Problems that arise are related to the nature of image data that spends a lot of memory. In this paper will compare 31 wavelet function by looking at its impact on PSNR, compression ratio, and bits per pixel (bpp) and the influence of decomposition level of PSNR and compression ratio. Based on testing performed, Haar wavelet has the advantage that is obtained PSNR is relatively higher compared with other wavelets. Compression ratio is relatively better than other types of wavelets. Bits per pixel is relatively better than other types of wavelet

Error Resilient Video Coding Using Bitstream Syntax And Iterative Microscopy Image Segmentation

There has been a dramatic increase in the amount of video traffic over the Internet in past several years. For applications like real-time video streaming and video conferencing, retransmission of lost packets is often not permitted. Popular video coding standards such as H.26x and VPx make use of spatial-temporal correlations for compression, typically making compressed bitstreams vulnerable to errors. We propose several adaptive spatial-temporal error concealment approaches for subsampling-based multiple description video coding. These adaptive methods are based on motion and mode information extracted from the H.26x video bitstreams. We also present an error resilience method using data duplication in VPx video bitstreams. A recent challenge in image processing is the analysis of biomedical images acquired using optical microscopy. Due to the size and complexity of the images, automated segmentation methods are required to obtain quantitative, objective and reproducible measurements of biological entities. In this thesis, we present two techniques for microscopy image analysis. Our first method, “Jelly Filling” is intended to provide 3D segmentation of biological images that contain incompleteness in dye labeling. Intuitively, this method is based on filling disjoint regions of an image with jelly-like fluids to iteratively refine segments that represent separable biological entities. Our second method selectively uses a shape-based function optimization approach and a 2D marked point process simulation, to quantify nuclei by their locations and sizes. Experimental results exhibit that our proposed methods are effective in addressing the aforementioned challenges

A human in-vivo model of acute resolving inflammation to study the pathogenesis of Rheumatoid Arthritis

Background Rheumatoid arthritis (RA) is a systemic autoimmune disease. The hypothesis of this study is that defects in the resolution of episodes of acute inflammation may contribute to the pathogenesis of this disease. The primary aim of this thesis is to establish if there is a difference in the onset and resolution of acute inflammation in patients with RA and Osteoarthritis (OA) compared to healthy controls. Methods A human model of acute, spontaneously resolving inflammation in response to intradermal ultra-violet light killed E.coli (UVkEC) has been developed. The use of suction blisters enables study of the whole coordinated immune response from onset through to resolution. This UVkEc blister model was used to assess the innate immune response in a cohort of healthy females. After demonstrating the acceptability to human volunteers, it was translated into participants with RA and OA and healthy control groups. The systemic and local response was assessed; encompassing clinical parameters as well as the response at the level of cells, proteins and lipids to identify a defect in the resolution of inflammation. Results This research identified key differences between the innate immune response of healthy human females and males, encompassing local blood flow, influx of haematopoietic cells, and production of inflammatory cytokines. Study of the innate immune response in participants with RA demonstrated no evidence of defective resolution of inflammation in the indices measured. In fact, there was a trend towards quicker or more efficient resolution. The response in participants with OA resembled that of RA participants. Conclusions This work demonstrates the practicability and challenges of using the UVkEc blister model in patients. The differences identified between males and females may be relevant to the prevalence of autoimmune disease in the different sexes and highlights the need for including both sexes in the early phases of research. Differences identified in the immune response of participants with RA and OA can be extended to further understand the pathogenesis of these diseases and may contribute to the debate surrounding the inflammatory component of OA

Glucocorticoids

As one class of the most important steroid hormones, glucocorticoids have long been recognised and their therapeutic benefits have been widely used in clinical treatment, especially in anti-inflammation cases. Glucocorticoids regulate various processes in the body including the mobilization of energy stores, immune functions, gene expression, and maintenance of the homeostasis as well as the stress response, this is not surprising that the concept of "glucocorticoids" is mentioned in almost all medical text books that focus on specific organs or systems such as the cardiovascular system, the immune system, and the neuroendocrine system. The book of Glucocorticoids - New Recognition of Our Familiar Friend aims to introduce the latest findings relating to glucocorticoids, either freshly from the laboratory or from clinical case studies, and to open up a new angle of looking at the issue of balancing the therapeutic benefits and side effects brought up by glucocorticoids