2 research outputs found
Breast Cancer Detection Using Multilevel Thresholding
This paper presents an algorithm which aims to assist the radiologist in
identifying breast cancer at its earlier stages. It combines several image
processing techniques like image negative, thresholding and segmentation
techniques for detection of tumor in mammograms. The algorithm is verified by
using mammograms from Mammographic Image Analysis Society. The results obtained
by applying these techniques are described.Comment: 5 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423,
http://sites.google.com/site/ijcsis
Segmentation of Breast Regions in Mammogram Based on Density: A Review
The focus of this paper is to review approaches for segmentation of breast
regions in mammograms according to breast density. Studies based on density
have been undertaken because of the relationship between breast cancer and
density. Breast cancer usually occurs in the fibroglandular area of breast
tissue, which appears bright on mammograms and is described as breast density.
Most of the studies are focused on the classification methods for glandular
tissue detection. Others highlighted on the segmentation methods for
fibroglandular tissue, while few researchers performed segmentation of the
breast anatomical regions based on density. There have also been works on the
segmentation of other specific parts of breast regions such as either detection
of nipple position, skin-air interface or pectoral muscles. The problems on the
evaluation performance of the segmentation results in relation to ground truth
are also discussed in this paper.Comment: 9 pages, 2 figures,IJCSI International Journal of Computer Science
Issues, Vol. 9, Issue 4, No 2, July 201