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Abstract #4802

Automatic Glioma Segmentation Algorithm Based on Superpixel Features

Yaping Wu1,2, Yusong Lin2, Guohua Zhao2, Longfei Li2, and Meiyun Wang3

1School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi'an, China, 2Collaborative Innovation Center for Internet Healthcare and School of Software and Applied Technology, Zhengzhou University, Zhengzhou, China, 3Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, China

This study proposes an algorithm to locate and segment Glioma tumor automatically. The algorithm contains three main steps. Firstly, a self-adaptation simple linear iterative clustering (ASLIC0) algorithm was executed to segment T2 weighted MRI images to superpixels images. Then, 52 features including fractal features, curvature feature and higher order derivative map Haralick texture features was calculated on each superpixel. Finally, a Support Vector Machine was trained as a classifier to select superpixels belong to tumor lesion or not. The Dice overlap measure for the segmented Glioma is 0.87 on the data set from the Henan Provincial People’s Hospital.

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