Modified Kernel Based Fuzzy Clustering for MR Brain Image Segmentation using Deep Learning
Kalyanapu Srinivas1, B. R. S. Reddy2

1Dr. Kalyanapu Srinivas*, Department of Computer Science and Engineering, Gudlavalleru Engineering College, Gudlavalleru, Andhra Pradesh, India.
2Dr. B. R. S. Reddy, Department of Computer Science and Engineering, Sri Vasavi Institute of Engineering and Technology, Pedana, Krishna Dist., Andhra Pradesh, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2881-2887 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8790088619/2019©BEIESP | DOI: 10.35940/ijeat.F8790.088619
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Abstract: The segmentation procedure might cause error in diagnosing MR images due to the artifacts and noises that exist in it. This may lead to misclassifying normal tissue as abnormal tissue. In addition, it is also required to model the ontogenesis of a tumour, as they propagate at distinctive rates in contrast to their surroundings. Hence, it is still a challenging task to segment MR brain images due to possible noise presence, bias field and impact of partial volume. This article presents an efficient approach for segmenting MR brain images using a modified kernel based fuzzy clustering (MKFC) algorithm. In addition, this approach computes the weight of each picture element based on the local mutation coefficient (LMC). The proposed system would reflexively group normal tissues like white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) respectively, from abnormal tissue, such as a tumour region, in MR brain images. Simulation outcomes have shown that the performance of the proposed segmentation approach is superior to the existing segmentation algorithms in terms of both ocular and quantitative analysis.
Keywords: MR brain image, tumour, data clustering, image segmentation, fuzzy-c means algorithm, gaussian kernel, deep learning convolutional neural networks.