A semi-automatic brain tumor segmentation algorithm
Xiaoli Zhang, Xiongfei Li, Hongpeng Li, Yuncong Feng · 2016
In this paper, a novel semi-automatic segmentation algorithm is proposed to segment brain tumors from magnetic resonance imaging (MRI) images. First, an edge-aware filter is used to get the smoothed version of the original image. Secondly, Otsu based multilevel thresholding is performed on the smoothed image and the original image, respectively. Then the two segmentation maps are fused by the rule of K Nearest Neighbors (KNN) to obtain the refined segmentation result. The combination of the three steps can be denoted as multi-scale Otsu based segmentation. Finally, a bi-directional region growing method is employed to segment the brain tumor region around seeds which are inserted by the user. The proposed algorithm is tested on MRI-T2 images and it produces promising result: the segmented tumor regions are more accurate compared to those obtained by other state-of-the-art methods.