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This research paper proposes an intelligent classification technique to recognize normal and abnormal MRI brain image. Medical image like ECG, MRI and CT-scan images are important way to diagnose disease of human being efficiently. The manual analysis of tumor based on visual inspection by radiologist/physician is the conventional method, which may lead to wrong classification when a large number...
Magnetic Resonance Imaging (MRI) results in overall quality that usually calls for human intervention in order to correctly identify details present in the image. More recently, interest has arisen in automated processes that can adequately segment medical image structures into substructures with finer detail than other efforts to-date. Relatively few image processing methods exist that are considered...
The main goal of the paper is to explore hierarchical classification. The investigation is performed on the dataset of Magnetic Resonance Images (MRI) which is hierarchically organized. Generalized top-down hierarchical classification architecture is proposed in the paper. Additionally, two specific cases of the generalized architecture are explored: three-stage hierarchical architecture based on...
Rapid image acquisition in magnetic resonance imaging (MRI) offers a number of potential benefits, such as avoiding physiological effects or scanning time on patients, overcoming physical constraints inherent within the MRI scanner, or meeting timing requirements when imaging dynamic structures and processes. In this paper, the combination of compressed sensing and parallel magnetic resonance imaging...
A Brain Cancer Detection and Classification System has been designed and developed. The system uses computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network in MRI images of different patients with Astrocytoma type of brain tumors. The image processing techniques such as histogram equalization, image segmentation, image enhancement,...
The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors are applied to provide feature extraction from the...
Magnetic resonance image (MRI) has been widely used for clinical applications in recent years. With the ability of scanning the same section by multiple frequencies, MRI makes it possible to generate several images on the same section. Despite of accessible abundant information, MRI also makes it more difficult to judge the location of every tissue. MRI will complicate the judgment due to strong noise...
A new approach to the design of uniplanar gradient coils for fully open magnetic resonance imaging (MRI) system with horizontal directed field is proposed to build a specified gradient magnetic field in a target region. In this new methodology, the surface current density of the coil is represented by a two-dimensional Fourier series expansion. A cost function is constructed in terms of the summation...
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