Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model

Arthanari Rajendran, Raghavan Dhanasekaran


Deformable or snake models are extensively used for medical image segmentation, particularly to locate tumor boundaries in brain tumor MRI images. Problems associated with initialization and poor convergence to boundary concavities, however, has limited their usefulness. As result of that they tend to be attracted towards wrong image features. In this paper, we propose a method that combine region based fuzzy clustering called Enhanced Possibilistic Fuzzy C-Means (EPFCM) and Gradient vector flow (GVF) snake model for segmenting tumor region on MRI images. Region based fuzzy clustering is used for initial segmentation of tumor then result of this is used to provide initial contour for GVF snake model, which then determines the final contour for exact tumor boundary for final segmentation. The evaluation result with tumor MRI images shows that our method is more accurate and robust for brain tumor segmentation.


Deformable model; FCM; Segmentation; MRI image; GVF

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