FCM Algorithm for the Research of Intensity Image Segmentation

丁震; 胡钟山; 杨静宇; 唐振民

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FCM Algorithm for the Research of Intensity Image Segmentation

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- FCM Algorithm for the Research of Intensity Image Segmentation
- Acta Electronica Sinica Issue 5, (1997)
- 作者机构：
  
  南京理工大学信息分院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.41
- Published：1997
- 稿件说明：
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[1]丁震,胡钟山,杨静宇,唐振民.FCM算法用于灰度图象分割的研究[J].电子学报,1997(05):39-43.

丁震, 胡钟山, 杨静宇, et al. FCM Algorithm for the Research of Intensity Image Segmentation[J]. Acta Electronica Sinica, 1997, (5).
[1]丁震,胡钟山,杨静宇,唐振民.FCM算法用于灰度图象分割的研究[J].电子学报,1997(05):39-43. DOI：

丁震, 胡钟山, 杨静宇, et al. FCM Algorithm for the Research of Intensity Image Segmentation[J]. Acta Electronica Sinica, 1997, (5). DOI：

摘要

模糊Ｃ均值（ＦＣＭ）算法用于灰度图象分割是一种非监督模糊聚类后再标定的过程，适合灰度图象中存在着模糊和不确定性的特点．但是这种算法存在着一些不足，如聚类数目无法自动确定、运算的开销太大等，因而限制了这种方法的应用．针对这些问题，本文利用直方图分析的方法，自动确定算法的聚类数目和各类的类峰值．并针对ＦＣＭ算法和灰度图象的特点，提出了一种适用于灰度图象分割的快速ＦＣＭ算法（ＱＦＣＭ），使得运算的开销降低，聚类分割的速度显著提高，并从数学和实验上证明了该方法的有效性．

Abstract

It is a procedure of the label following an unsupervised fuzzy clustering that fuzzyc-means （FCM） algorithm is applied for intensity image segmentation

and it suits for the uncertainand ambiguous characteristic in intensity image. However

there are some deficiencies in the algorithm

for example

the number of clustering can not be determined automatically and the operational cost

for large data sets

is too high

which limit its application. In order to overcome the deficiencies

the number of clustering and the maximum of each cluster are automatically determinedthrough the histogram analysis ; Then

according to the character of FCM and intensity image

aquick fuzzy C-means （QFCM ） algorithm is presented for intensity images segmentation

with whichthe operational cost is reduced and the speed of clustering segmentation is greatly increased. Thefeasibility of the approach is proved by the mathematics and experiment result.

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