Segmentation of medical ultrasound images using an adaptive magnetostatic bidirectional balloon snake model

Minfen Shen; Qiong Zhang; Boling Zheng

doi:10.1117/12.2205828

3 December 2015 Segmentation of medical ultrasound images using an adaptive magnetostatic bidirectional balloon snake model

Minfen Shen, Qiong Zhang, Boling Zheng

Author Affiliations +

Proceedings Volume 9794, Sixth International Conference on Electronics and Information Engineering; 97941T (2015) https://doi.org/10.1117/12.2205828
Event: Sixth International Conference on Electronics and Information Engineering, 2015, Dalian, China

Abstract

Medical ultrasound imaging is one of the most effective methods for lesion early detection. However, the segmentation of lesions such as breast cancers from ultrasound images is still a challenging task due to the variance in shape of the lesions and interference from speckle noise. In this paper, a novel approach using a generalized adaptive magnetostatic bidirectional balloon snake (AMBBS) model is adopted for lesion segmentation. Experimental results show that the generalized adaptive magnetostatic bidirectional balloon snake is robust to speckle noise and can converge to complex geometries and weak edges successfully.

Citation Download Citation

Minfen Shen, Qiong Zhang, and Boling Zheng "Segmentation of medical ultrasound images using an adaptive magnetostatic bidirectional balloon snake model ", Proc. SPIE 9794, Sixth International Conference on Electronics and Information Engineering, 97941T (3 December 2015); https://doi.org/10.1117/12.2205828

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