Unsupervised learning algorithms for MRI brain tumor segmentation

The structure of the brain can be seen by the Magnetic Resonance (MR) image output. MR scanned image of the brain is utilized for the entire study in this paper. The MR image filter is more agreeable than some other outputs for analysis. It will not influence the human body since it does not hone any radiation. In digitization of MR scanned image, segmentation of brain tumor is one kind of challenging problems and it is critical to clinical diagnosis. So segmentation needs to be accurate, robust, and efficient to avoid impacts caused by various large and complex biases added to images. Clustering algorithms have been widely used for the segmentation. In this paper, the K-means (KM) clustering and Fuzzy C-means (FCM) clustering algorithms are used to locate the tumor and extract it. Comparative analysis in terms of Segmented area, Relative area, Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) is performed between K-means clustering and FCM clustering algorithms. The obtained performance measures from the experiments indicate the superiority of the chosen FCM algorithm over the K-means algorithm. That is 0.93% of relative segmented tumor area for FCM shows that the area which was effected by the tumor in the original MR image is segmented as a tumor. The FCM Algorithm has less processing time of 8.639 seconds compared to 22.831 seconds for KM algorithm.

[1]  Mohammed Elmogy,et al.  Brain tumor segmentation based on a hybrid clustering technique , 2015 .

[2]  M. Karnan,et al.  Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[3]  K. S. Thara,et al.  Brain tumour detection in MRI images using PNN and GRNN , 2016, 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[4]  Loay Kadom Abood,et al.  Detecting brain tumor in Magnetic Resonance Images using Hidden Markov Random Fields and Threshold techniques , 2014, 2014 IEEE Student Conference on Research and Development.

[5]  N. Santhiyakumari,et al.  Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain , 2015 .

[6]  Sukanta Sabut,et al.  Kernelized Fuzzy C-means Clustering with Adaptive Thresholding for Segmenting Liver Tumors , 2016 .

[7]  Kumar Rajamani,et al.  Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[8]  Rajoo Pandey,et al.  Noise adaptive FCM algorithm for segmentation of MRI brain images using local and non-local spatial information , 2015, 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA).

[9]  Pallikonda Rajasekaran Murugan,et al.  An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images , 2016, Appl. Soft Comput..

[10]  Mark Rosen,et al.  A Multichannel Markov Random Field Framework for Tumor Segmentation With an Application to Classification of Gene Expression-Based Breast Cancer Recurrence Risk , 2013, IEEE Transactions on Medical Imaging.

[11]  S.Ravi,et al.  Brain Tumor Segmentation Using K-MeansClustering And Fuzzy C-Means AlgorithmsAnd Its Area Calculation , 2014 .

[12]  Amit Pimpalkar,et al.  Detection of brain tumor from MRI images by using segmentation & SVM , 2016, 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave).

[13]  Suresh S. Salankar,et al.  Modified Fuzzy C Means with Optimized Ant Colony Algorithm for Image Segmentation , 2015, 2015 International Conference on Computational Intelligence and Communication Networks (CICN).