Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization

Abstract

The automatic brain tumor segmentation in MRI (Magnetic Resonance Images) is becoming a challenging task in the field of medicine, since the brain tumor occurs in different shapes, intensities and sizes. This paper proposes an efficient automatic brain tumor segmentation using Greedy Snake Model and Fuzzy C-Means optimization. This method initially identifies the approximate Region Of Interest (ROI), by removing the non-tumor part by two level morphological reconstruction such as dilation and erosion. A mask is formed by thresholding the reconstructed image and is eroded to improve the accuracy of segmentation in Greedy Snake algorithm. Using the mask boundary as initial contour of the snake, the greedy snake model estimates the new boundaries of tumor. These boundaries are accurate in regions where there is sharp edge and are less accurate where there are ramp edges. The inaccurate boundaries are further optimized by using Fuzzy C-Means algorithm to obtain the accurate segmentation output. The region that has large perimeter is finally chosen, to eliminate the in- accurate segmented regions. The experimental verification were done on T1-weighted contrast-enhanced image data set, using the metrics such as dice score, specificity, sensitivity and Hausdorff distance. The proposed method outperforms when compared with the traditional brain tumor segmentation methods in MRI images.

Keywords

• MRI images
• Image segmentation
• Fuzzy C-means algorithm
• Greedy snake model