Abstract: During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy for the purpose of initiating and speeding up the recovery process. The Medical Image segmentation deals with segmentation of tumor in MR images for improved quality in medical diagnosis. It is very important and a challenging problem due to noise present in input images while doing image analysis. An efficient algorithm is proposed in this paper for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced to remove noise and then morphological operators are applied to detect the tumor in the scanned image. Here the segmentation is carried out using k-means and fuzzy c-means clustering algorithm for better performance. Tumour can be found with more precision and also fast detection is achieved with only few seconds for execution and the area of the tumour can also be analyzed.
Keywords: Brain tumor, image processing, K-means clustering, Fuzzy c-means clustering, MR image, Segmenta- tion.
Title: Segmentation of MRI Image for the Detection of Brain Tumour
Author: G. Nagaraju, Dr. P. V. Ramaraju, A.Nandini, G.P.P.S. Viswanadh Varma, K.P.M. Mounika, Md.Akram Baig
International Journal of Electrical and Electronics Research
ISSN 2348-6988 (online)
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