Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (8): 111-117.doi: 10.11896/jsjkx.200700003

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Improved FCM Brain MRI Image Segmentation Algorithm Based on Tamura Texture Feature

QIAO Ying-jing, GAO Bao-lu, SHI Rui-xue, LIU Xuan, WANG Zhao-hui   

  1. College of Software,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
  • Received:2020-07-01 Revised:2020-08-13 Published:2021-08-10
  • About author:QIAO Ying-jing,born in 1995,postgra-duate.Her main research interests include intelligent & adaptive control and image processing.(2068195322@qq.com)GAO Bao-lu,born in 1971,Ph.D,lectu-rer,postgraduate supervisor.His main research interests include intelligent information processing and image processing.
  • Supported by:
    National Key Research and Development Program of China(2018YFB2200900) and National Natural Science Foundation of China(61772358).

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Abstract: To solve the problems of noise sensitivity and initial clustering center randomness in the segmentation of brain MRI images by FCM algorithm,an improved FCM image segmentation algorithm based on Tamura texture feature is proposed.Firstly,the Tamura texture feature of the image is extracted,and it is linearly weighted with the gray feature to form a fusion feature.Then,the density of pixel is calculated by using fuzzy neighborhood relation,and the initial cluster center is selected by combining it with distance relation.Finally,the fusion feature is used as a feature constraint for updating membership and clustering center.In the experiment,FCM,D-FCM,WKFCM and the proposed method are used to segment the images in Brain Web MRI dataset,and their anti-noise performance,accuracy and operation efficiency are compared.Experimental results show that the proposed algorithm can obtain better initial clustering centers,has better robustness in processing noise and gray inhomogeneity images,and can segment brain MRI images quickly and effectively.

Key words: Fuzzy clustering, Initial cluster center, Linear fusion, Magnetic resonance imaging, Tamura texture information

CLC Number: 

  • TP751
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