Empirical Study of Vessel Extraction Algorithms

Medical imaging is a technique for creating an image of the human body in order to diagnose various diseases such as stenosis, aneurysm, arterial venous malformation, thrombus, plaque and internal bleeding. Blood vessel segmentation is critical in the diagnosis of a variety of diseases. Blood vessels that are segmented give much useful information about their anatomy and location. They are important in a variety of medical applications, including diagnostic, surgical therapy, and radiation treatments.  A significant amount of research has gone into vessel segmentation, and a variety of techniques has emerged as a result. In addition, there are different segmentation techniques such as active contour segmentation technique, hybrid segmentation technique, thresholding segmentation techniques, watershed segmentation techniques, edge detection segmentation technique, etc. It is also observed that magnetic resonance images of blood vessels were exposed to noise due to selection and inappropriate techniques such poor performance invisibility. In other words, there is no single approach to follow for a perfect outcome of images. There are some of the methods that use gray-level histograms, while there are others that integrate spatial image information, and this causes noisy outcomes. Therefore, we build the medical imaging vessel visualization system using MATLAB as tool. In this study, we empirically investigate the visibility performance vessel extraction algorithm. We implement following vessel extraction algorithms: active contour algorithm and edge detection algorithm. We observed that edge detection algorithm (SOBEL) is the better in term of image clarity as compared to active contour and edge detection algorithm. This project enable IS department to do more advanced level research in medical imaging

Empirical Study of MRI Brain Tumor Edge Detection Algorithms

A brain tumor refers to the abnormal growth of cells that can be found in the brain or the skull. MRI is a type of advanced medical imaging that provides detailed information about the anatomy of the human soft tissues. Medical experts perform tumor segmentation using magnetic resonance imaging (MRI) data, which is an essential part of cancer diagnosis and treatment. Tumor detection refers to the methods that are used to diagnose cancer or other types of diseases. Edge detection is also one of the common methods that come under the process of treating medical images. The main objective of edge detection is discovering information about the shapes, transmission, and reflection of images. In this paper, we investigated the performance comparison MRI brain tumor edge detection Algorithms. The Canny, and Prewitt are used for investigation. As result, Canny edge detection is better than Prewitt in term of clarity and visibility for the tumor

A New Parallel Genetic Algorithm Model

This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. These three models are: a sequential model, a modified global parallel genetic algorithm model and a new proposed parallel genetic algorithm model we called the Trigger Model (TM). The reduction of the implementation processing time is the basic motivation of genetic algorithms parallelization. In this work, parallel virtual machine (PVM), which is a portable message-passing programming system, designed to link separate host machines to form a virtual machine which is a single, manageable computing resource, is used in a distributed heterogeneous environment. The best result was reached and The TM model was clearly performing better than the other two models

Enhanced Inflammatory Potential of CD4(+) T-Cells That Lack Proteasome Immunosubunit Expression, in a T-Cell Transfer-Based Colitis Model

Proteasomes play a fundamental role in intracellular protein degradation and therewith regulate a variety of cellular processes. Exposure of cells to (pro)inflammatory cytokines upregulates the expression of three inducible catalytic proteasome subunits, the immunosubunits, which incorporate into newly assembled proteasome complexes and alter the catalytic activity of the cellular proteasome population. Single gene-deficient mice lacking one of the three immunosubunits are resistant to dextran sulfate sodium (DSS)-induced colitis development and, likewise, inhibition of one single immunosubunit protects mice against the development of DSS-induced colitis. The observed diminished disease susceptibility has been attributed to altered cytokine production and CD4+ T-cell differentiation in the absence of immunosubunits. To further test whether the catalytic activity conferred by immunosubunits plays an essential role in CD4+ T-cell function and to distinguish between the role of immunosubunits in effector T-cells versus inflamed tissue, we used a T-cell transfer-induced colitis model. Naïve wt or immunosubunit-deficient CD4+ T-cells were adoptively transferred into RAG1-/- and immunosubunit-deficient RAG1-/- mice and colitis development was determined six weeks later. While immunosubunit expression in recipient mice had no effect on colitis development, transferred immunosubunit-deficient T- cells were more potent in inducing colitis and produced more proinflammatory IL17 than wt T-cells. Taken together, our data show that modifications in proteasome-mediated proteolysis in T-cells, conferred by lack of immunosubunit incorporation, do not attenuate but enhance CD4+ T-cell-induced inflammation

A Practical Performance Comparison of Parallel Sorting Algorithms on Homogeneous Network of Workstations

Abstract: Three parallel sorting algorithms have been implemented and compared in terms of their overall execution time. The algorithms implemented are the odd-even transposition sort, parallel merge sort and parallel rank sort. A homogeneous cluster of workstations has been used to compare the algorithms implemented. The MPI library has been selected to establish the communication and synchronization between the processors. The time complexity for each parallel sorting algorithm will also be mentioned and analyzed

A Hybrid Approach of Using Symmetry Technique for Brain Tumor Segmentation

Tumor and related abnormalities are a major cause of disability and death worldwide. Magnetic resonance imaging (MRI) is a superior modality due to its noninvasiveness and high quality images of both the soft tissues and bones. In this paper we present two hybrid segmentation techniques and their results are compared with well-recognized techniques in this area. The first technique is based on symmetry and we call it a hybrid algorithm using symmetry and active contour (HASA). In HASA, we take refection image, calculate the difference image, and then apply the active contour on the difference image to segment the tumor. To avoid unimportant segmented regions, we improve the results by proposing an enhancement in the form of the second technique, EHASA. In EHASA, we also take reflection of the original image, calculate the difference image, and then change this image into a binary image. This binary image is mapped onto the original image followed by the application of active contouring to segment the tumor region

A New Parallel Genetic Algorithm Model

This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. These three models are: a sequential model, a modified global parallel genetic algorithm model and a new proposed parallel genetic algorithm model we called the Trigger Model (TM). The reduction of the implementation processing time is the basic motivation of genetic algorithms parallelization. In this work, parallel virtual machine (PVM), which is a portable message-passing programming system, designed to link separate host machines to form a virtual machine which is a single, manageable computing resource, is used in a distributed heterogeneous environment. The best result was reached and The TM model was clearly performing better than the other two models