Abstract
Magnetic resonance imaging (MRI) plays a vital role among the advanced techniques for the imaging of internal organs. It is the least harmful method compared to other existing medical imaging techniques like computed tomography scan, X-ray etc. Image segmentation is the basic step to analyse images and hence to extract data from them. In this paper, we concentrate on brain MRI segmentation, where the performance of algorithms such as k-mean, fuzzy-c-mean (FCM) and their combination (k-FCM) is evaluated. In the proposed methodology, MR brain images of different tumor types like meningioma, sarcoma, glioma, etc. are preprocessed and separate segmentation are being performed using k-mean and FCM methods. Further, the k-mean segmented image is given to the FCM and their performance is compared. The hybrid segmentation scheme gives better results for extraction of tumor regions. The segmented image can be given to a good classifier to detect tumor types and hence the physicians can execute better treatment.
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Ahammed Muneer, K.V., Paul Joseph, K. (2018). Performance Analysis of Combined k-mean and Fuzzy-c-mean Segmentation of MR Brain Images. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_71
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DOI: https://doi.org/10.1007/978-3-319-71767-8_71
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