A texture analysis method for detection of clustered microcalcifications on digital mammograms
by M.C. Barretto; D.A. Kulkarni; G.R. Udupi
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 8, No. 5/6, 2012

Abstract: Breast cancer is one of the major causes of death among women. Early detection of breast cancer is possible by the detection of clustered microcalcifications on X-ray mammograms. Texture is an important characteristic used in identifying objects or region of interest in a digitised mammogram. This work focuses on a statistical texture analysis method called Surrounding Region Dependence Method (Kim and Park, 1999) - based on second order histogram in two surrounding regions. Six textural features are extracted and are used to classify region of interests into positive ROIs, containing clustered microcalcifications and negative ROIs composed of normal breast tissues. A 3-layer backpropagation neural network is used as a classifier. Results are evaluated using Receiver Operating Characteristics analysis.

Online publication date: Wed, 10-Oct-2012

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Bioinformatics Research and Applications (IJBRA):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your 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