FCM-Based Method for MRI Segmentation of Anatomical Structure

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• 关键词：Magnetic Resonance Imaging (MRI) ; Fuzzy C ; means (FCM) ; Image segmentation ; Spatial constraint ; Expert knowledge ; Medical imaging
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9656
• 期：1
• 页码：175-183
• 全文大小：624 KB
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• 作者单位：Pinti Antonio (15) (16)
  
  15. University of Lille Nord de France, UVHC, 59313, Valenciennes, France
  16. EA 4708, I3MTO, CHRO, 1 rue Porte Madeleine, 45032, Orléans, France
  
• 丛书名：Bioinformatics and Biomedical Engineering
• ISBN：978-3-319-31744-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Fuzzy C-means (FCM) has been widely applied to segmentation of medical images, especially MRI images for identifying living organs and supporting medical diagnosis. However, in practice, this method is too sensitive to image noises. Then, many methods have been proposed to improve the objective function of FCM by adding a penalty term to it. One drawback of these methods is that they can determine neither the appropriate size of observation window for each pixel of interest for incorporating spatial information, nor the suitable importance coefficient of the penalty term. Moreover, the modification of the objective function of FCM often causes additional complex derivations. In this paper, we develop a new FCM-based method for medical MRI image segmentation. This method permits to dynamically determine the optimal size of observation window for each pixel of interest without adding any penalty term. Moreover, a n-dimensional feature vector including both local and global spatial information between neighboring pixels is generated to describe each pixel in the objective function. And specialized a priori knowledge is integrated into the segmentation procedure in order to control the application of FCM for tissue classification of thigh. The effectiveness and the robustness of the proposed method have been validated by real MRI image of thigh.