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IJSTR >> Volume 8 - Issue 7, July 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Novel Center Symmetric Local Binary Pattern And Chi Square Fuzzy C-Mean Clustering Based Segmentation In Medical Imaging Technique

[Full Text]

 

AUTHOR(S)

G. Anand Kumar, P. V. Sridevi

 

KEYWORDS

image clustering, brain tumor segmentation, fuzzy algorithm, threshold, magnetic resonance imaging

 

ABSTRACT

Accurate brain tumor segmentation is a challenging task from the Magnetic Resonance Imaging (MRI) in the field of medical image processing. For this purpose, we propose a Novel Center Symmetric-Local Binary Pattern (CS-LBP) and Chi Square Fuzzy C-mean based segmentation via clustering to segment the abnormal tissues from the normal region. Initially, preprocessing is performed to extricate the region of interest based on improved threshold and center symmetric LBP. 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 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.

 

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