Abstract
Automated brain tumor detection from MRI images is a very challenging job in today's modern medical imaging research. MRI is used to take image of soft tissues of human body. It is very helpful for analyzing human organs without surgical intervention. For automatic detection of tumor, segmentation of brain image is required. Segmentation partitions the image into distinct regions based on various parameters. It is the most important and challenging area of computer aided clinical diagnostic tools. Although Conventional segmentation approaches are computationally efficient, but have low quality of edge and feature detection. Here, we propose an algorithm on frame theoretic methods and the discrete wavelet transform and apply it to brain MRIs. Significant gains in performance are observed over conventional segmentation algorithms.
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