Mass segmentation in mammograms using a kernel-based fuzzy level set method Online publication date: Sun, 08-Nov-2015
by J. Anitha; J. Dinesh Peter
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 19, No. 2, 2015
Abstract: In this paper, a Kernel-Based Fuzzy Level Set (KFLS) method is proposed to facilitate the automatic segmentation of lesions in mammogram image. Initially, the mammogram image is pre-processed to remove the noises and to extract the breast profile. Kernel-based Fuzzy C-Means (KFCM) segmentation is applied to segment the pre-processed image into a number of clusters based on the breast structure. The cluster with Region of Interest (ROI) is automatically identified to remove background and normal tissues. Finally, the mass region extracted from the KFCM is used as an initial contour for the level set segmentation to refine the mass boundary. The result of KFCM is also used to automatically initialise the control parameters of level set segmentation algorithm. This methodology is validated on 300 mammogram images of DDSM database with diagnosed mass. The experimental results demonstrate that the proposed method achieves a sensitivity of 93.32% and accuracy of 94.31%.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook