摘要
为了简单有效地获得理想的医学图像边缘,进行医学诊断,提出了一种基于模糊子集组合的图像边缘检测方法．由于图像边缘的模糊性,在边缘检测过程中应用了模糊集运算方法:先将图像的灰度直方图划分成相应的几个不同的子区,并对与灰度方差直方图子区相应的模糊子集进行运算,综合运算结果,最终得到图像的边缘．文中实例及对几种方法的比较表明,提出的Fuzzy算子所得到的图像边缘优于Sobel算子和Prewitt算子时所得到的图像边缘．
In order to achieve simply and effectively the ideal edge of a medical image and thus perform an effective medical diagnose, a fast edge detection method based on the combination of fuzzy subsets is developed. We partition an image into two portions： the edge portion and the non-edge portion. Non-edge portion, consisting of the objects and its background, is removed from an image; the remainder is image＇s edge. Considering the fuzziness of image＇s edge, some fuzzy operations can be carried out. We firstly partition the gray-level histogram into several sub-regions and operate corresponding fuzzy subsets. Finally, image＇s edge is obtained. Experiment results show as compared with Sobel operator or Prewitt operator, that the described method is more excellent.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2005年第6期647-651,共5页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(10072006)
关键词
医学图像边缘检测
模糊子集组合
灰度直方图
灰度方差直方图
edge detection of medical image
combination of fuzzy subsets
gray level histogram
gray-level-square-differenee histogram
