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The count of tumor patients is increasing day by day. Brain tumor, whose main cause is the uncontrolled division of the cells, if detected at an early stage, will help a lot in curing it. Various detection techniques are available for identifying the abnormality in the brain, but, MRI is a better technique in comparison to others. This paper presents a method for distinguishing the tumor affected...
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...
Image segmentationisan important process to extract information from complex medical images. Segmentation has wide application in medical field. The main objective of image segmentation is to partition an image into mutually exclusive and exhausted regions such that each region of interest is spatially contiguous and the pixels within the region are homogeneous with respect to a predefined criterion...
Accurate spleen segmentation in abdominal MRI images is one of the most important steps for computer aided spleen pathology diagnosis. The first and essential step for the diagnosis is the automatic spleen segmentation that is still an open problem. In this paper, we have proposed a new automatic algorithm for spleen area extraction in abdominal MRI images. The algorithm is fully automatic and contains...
Automatic Segmentation of brain MRI is used as a diagnostic tool in neuro medicine. Abnormal growth of brain tissues can be detected. Changes in volumetric growth of brain tissues such as white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) can help in the early detection of neural disorders like epilepsy, Alzhemeirpsilas disease etc. Automatic segmentation of brain is a challenging problem...
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