Journal
MACHINE VISION AND APPLICATIONS
Volume 30, Issue 7-8, Pages 1111-1122Publisher
SPRINGER
DOI: 10.1007/s00138-019-01020-0
Keywords
Breast mass segmentation; Superpixel; Level set method; Local Gaussian distribution fitting
Categories
Funding
- Natural Science Foundation of High Education Institutions of Jiangsu Province, China [18KJB50030, 18KJB520042, 17KJB520033]
- National Natural Science Foundation of China [61502206, 61772277, 61672291]
- Nature Science Foundation of Jiangsu Province [BK20150523, BK20171494]
Ask authors/readers for more resources
In this paper, an effective method is proposed for breast mass segmentation using a superpixel generation and curve evolution method. The simple linear iterative clustering method and density-based spatial clustering of applications with noise method are applied to generate superpixels in mammograms at first. Thereafter, a region of interesting (ROI) that contains the breast mass is built on the superpixel generation results. Finally, the image patch and the position of the manual labeled seed are used to build the prior knowledge for the level set method driven by the local Gaussian distribution fitting energy and evolve the curve to capture the edge of breast mass in ROI. Experimental results on mammogram data set demonstrate that the proposed method shows superior performance in contrast to some well-known methods in breast mass segmentation.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available