IMPACT OF TRUNCATION AND ALIASING ARTIFACTS IN DETECTION OF CEREBELLAR LESION FROM MRI IMAGES

Bio-Medical Image Processing

Magnetic Resonance Imaging (MRI) is a medical imaging technique employed by radiologists to investigate the anatomy and physiology of the body. The dynamic growth in the computer aided medical image segmentation playing a very important part in technical and scientific research. It helps the doctors to diagnose the disease in an easy manner there by promoting quick decision making. MRI usually contains artifacts. Cerebellum lesion segmentation is very demanding topic. This is because of magnetic resonance images (MRI) may be affected due to extraneous noise, artifacts and intensity non-uniformity. Manual MRI segmentation is a very monotonous, time lingering and user-dependent job. The presence of artifacts reduces the quality of analysis and hence results in poor evaluation. To avoid diagnostic errors, artifacts ought to be removed or minimized. In this proposed work, we have used methods and algorithms to minimize the truncation and aliasing artifacts from the brain MR images and also, we have used segmentation using Modified Fuzzy C-Means with level set method to improve the segmentation result.

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