Earticle

초록

영어

The image segmentation technology is of great significance to the target identification. The watershed segmentation algorithm has wide application in image segmentation. The traditional watershed segmentation often causes the problems of over segmentation and noise sensitivity. Therefore, a medical image segmentation algorithm is proposed based on K-means clustering algorithm and improved watershed algorithm. First, K - means clustering algorithm is used for initial segmentation, and then the concept of similarity is put forward to improve the original watershed algorithm. Finally, the adjacent tiles of the initial segmentation is merged. The magnetic resonance image is regarded as the segmentation object. The experimental result shows that the proposed algorithm effectively solves the problem of the over-segmentation of traditional watershed algorithm, and achieves a satisfactory effect for the image segmentation.

목차

Abstract
 1. Introduction
 2. K-Means Clustering Algorithm
 3. Improved Watershed Segmentation Algorithm
 4. The Results and Analysis of the Experiments
 5. Conclusion
 References

키워드

저자

• BenZhai Hai [ College of Computer & Information Engineering, Henan Normal University, Xinxiang, Henan, China / Information Engineering college, Wuhan University Of Technology, Wuhan ,Hubei, China ]
• RuiYun Xie [ Department of Computer Science and Technology, Henan Institute of Technology, Xinxiang, Henan, China ]
• PeiYan Yuan [ College of Computer & Information Engineering, Henan Normal University, Xinxiang, Henan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605