Interactive segmentation method with graph cut and SVMs

Xing Zhang; Jie Tian; Dehui Xiang; Yongfang Wu

doi:10.1117/12.844257

12 March 2010 Interactive segmentation method with graph cut and SVMs

Xing Zhang, Jie Tian, Dehui Xiang, Yongfang Wu

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76234M (2010) https://doi.org/10.1117/12.844257
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Medical image segmentation is a prerequisite for visualization and diagnosis. State-of-the-art techniques of image segmentation concentrate on interactive methods which are more robust than automatic techniques and more efficient than manual delineation. In this paper, we present an interactive segmentation method for medical images which relates to graph cut based on Support Vector Machines (SVMs). The proposed method is a hybrid method that combines three aspects. First, the user selects seed points to paint object and background using a "brush", and then the labeled pixels/voxels data including intensity value and gradient of the sampled points are used as training set for SVMs training process. Second, the trained SVMs model is employed to predict the probability of which classifications each unlabeled pixel/voxel belongs to. Third, unlike traditional Gaussian Mixture Model (GMM) definition for region properties in graph cut method, negative log-likelihood of the obtained probability of each pixel/voxel from SVMs model is used to define t-links in graph cut method and the classical max-flow/min-cut algorithm is applied to minimize the energy function. Finally, the proposed method is applied in 2D and 3D medical image segmentation. The experiment results demonstrate availability and effectiveness of the proposed method.

Citation Download Citation

Xing Zhang, Jie Tian, Dehui Xiang, and Yongfang Wu "Interactive segmentation method with graph cut and SVMs", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76234M (12 March 2010); https://doi.org/10.1117/12.844257

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available