Abstract
In the past, doctors judged images based on their own medical knowledge. Nowadays, the digital image processing technology can alleviate the burden of judging a large amount of multispectral information and lead to more effective diagnosis of the pathological tissues. In this paper, we propose a new approach of seeded region growing based on extension (SRGBE) to classify tissues from brain MRI. Based on extension, we tried to strengthen the regional definition. First, we use seeded region growing (SRG) to segment brain images. Second, the SRGBE result is further classified by K-means. Finally, we compare the images of gray matter, white matter and cerebral spinal fluid produced by both approaches to demonstrate the performance of SRGBE.
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