东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (8): 1079-1084.DOI: 10.12068/j.issn.1005-3026.2017.08.004

• 信息与控制 • 上一篇    下一篇

改进的基于ASFCM婴幼儿脑部MRI分割算法

魏颖1,2， 张开1， 韩枫1   

  1. (1. 东北大学 信息科学与工程学院， 辽宁 沈阳110819; 2. 东北大学 医学影像计算教育部重点实验室， 辽宁 沈阳110179)
  • 收稿日期:2016-03-23 修回日期:2016-03-23 出版日期:2017-08-15 发布日期:2017-08-12
  • 通讯作者: 魏颖
  • 作者简介:魏颖(1968-)，女，辽宁本溪人，东北大学教授，博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61370152); 中央高校基本科研业务费专项资金资助项目(N130204003).

Improved ASFCM-based Algorithm for Infant Brain MRI Segmentation

WEI Ying1,2， ZHANG Kai1， HAN Feng1   

  1. 1. School of Information Science & Engineering， Northeastern University， Shenyang 110179; 2. Key Laboratory of Medical Imaging Calculation of the Ministry of Education， Northeastern University， Shenyang 110179， China.
  • Received:2016-03-23 Revised:2016-03-23 Online:2017-08-15 Published:2017-08-12
  • Contact: ZHANG Kai
  • About author:-
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摘要： 在诸多FCM的改进算法中，ASFCM算法表现较好，该算法改变空间惩罚项结构，使目标函数连续，并且具有自适应的参数，但会出现无法抑制婴幼儿脑部MR图像噪声较大的问题.为了解决这个问题，在ASFCM算法基础上融合非局部权重和核函数思想，提出一种改进的ASFCM算法(KNL-ASFCM).采用本文算法，FCM，RFCM和ASFCM算法对加入不同种类和强度噪声的临床婴幼儿脑部MR图像进行实验.分析结果表明:本文算法的分割准确性和噪声抑制能力比其他三种算法均有一定的提高，对婴幼儿脑部MR图像分割问题具有明显优势.

关键词: 图像分割, 核函数, ASFCM, 婴幼儿脑部, MR图像

Abstract: Among many modified fuzzy c-mean (FCM) algorithms， the adaptive spatial fuzzy c-means (ASFCM) clustering algorithm is quite advantageous， as it has the adaptive parameters and changes the structure of spatial penalty to make the objective function continuous， but it cannot restrain the large noise contained by infant brain MR images. In response to this issue， we improve the ASFCM algorithm with non-local weights and the kernel function， which is named as the improved ASFCM algorithm with kernel function and non-local weights. Then， the FCM algorithm， RFCM algorithm， ASFCM algorithm and the algorithm we proposed are used to segment the clinical infant brain MR images with different kinds and intensities of noise. Results show that the segmentation accuracy and denoising ability of the proposed algorithm are greatly improved compared with the other three algorithms， and our algorithm has obvious advantages for the infant brain MR image segmentation.

Key words: image segmentation, kernel function, ASFCM, infant brain, MR image

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