Brain Tumor Segmentation Using Chi-Square Fuzzy C-Mean Clustering

  • G. Anand KumarEmail author
  • P. V. Sridevi
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


Accurate brain tumor segmentation is an interesting and challenging task of magnetic resonance imaging (MRI) in the field of medical image processing. For this purpose, we propose a chi-square fuzzy c-mean-based segmentation via clustering to segment the abnormal tissues from the normal region. Initially, based on improved threshold and center-symmetric LBP, the preprocessing is performed to extract the region of interest. Then, we compare the preprocessing output and original MRI image using Bhattacharya similarity metrics to obtain the region of interest from the imaging technology. Finally, chi square distance-based fuzzy c-mean (CS-FCM) segmentation is performed to cluster the region according to the feature based on the region of interest (ROI), including entropy, contrast, and mean for necrosis, edema, and enhanced tumor regions. BRATS 2015 dataset is used to evaluate the performance in terms of Jaccard matching, specificity, positive predictive value (PPV), and dice similarity coefficient (DSC). The existing approaches are not efficient and predictive, whereas our proposed method performs better in clustering the tumor into three regions (necrosis, edema, and enhanced tumor) based on the region of interest.


Image clustering Brain tumor segmentation Fuzzy algorithm Threshold Magnetic resonance imaging 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringAndhra University College of Engineering (Autonomous)VisakhapatnamIndia

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