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基于改进FCM的医学图像分割
引用本文:马华,王清,张永.基于改进FCM的医学图像分割[J].微计算机信息,2006,22(7):241-242.
作者姓名:马华  王清  张永
作者单位:1. 271000,山东,泰安,泰山医学院信息科学系
2. 730050,兰州理工大学计算机与通信学院
摘    要:为解决模糊C均值聚类(FCM)算法在图像分割尤其是医学图像分割中存在的计算量大、运行时间过长的问题,提出了一种改进方法。通过数据约减,即通过对相近的像素进行量化并聚合来减少像素个数,从而降低运算量。该方法用于人脑磁共振图像的分割比传统FCM算法的运算速度提高了50 ̄100多倍,并且选择合适大小的量化箱不会影响算法的分割效果。

关 键 词:数据约减  模糊C均值  磁共振成像  图像分割
文章编号:1008-0570(2006)03-1-0241-02
修稿时间:2005年8月6日

Medical Image Segmentation Based on Modified FCM Algorithm
Ma,Hua,Wang,Qing,Zhang,Yong.Medical Image Segmentation Based on Modified FCM Algorithm[J].Control & Automation,2006,22(7):241-242.
Authors:Ma  Hua  Wang  Qing  Zhang  Yong
Abstract:The fuzzy C means clustering (FCM) algorithm requires a long time to segment images, especially medical images, due to processing the large dataset. This paper discusses a modified algorithm based on data reduction, which is able to reduce the number of pixels by aggregating similar ones. The reduction in the amount of clustering data allows a partition of the data to be produced faster. The algorithm is applied to the problem of segmenting brain magnetic resonance images into different tissue types. Average speed- ups of as much as 50- 100 times a traditional implementation of fuzzy c- means were obtained, while producing partitions that are equivalent to those produced by fuzzy c- means.
Keywords:data reduction  fuzzy c- means  magnetic resonance imaging (MRI)  image segmentation
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