The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off


Nowadays the most common type of cancer in women is breast cancer. This is the second main cause of cancer deaths in women. Digital mammography is the technique which is used to examine the breast. This is very much useful for the detection of breast diseases in women. The automatic detection of tumor or some type of deformity in the medical imaging is done by many researchers to develop some algorithms and methods. In this paper we are using SOM and Fuzzy c-means clustering techniques for tumor detection in digital mammography images. We then further calculate the statistical features of tumor like location of tumor, area, energy, entropy, idm, mean, contrast, mean and standard deviation which helps the radiologist to study the statistical information regarding breast cancer, so that the doctors can give better treatment to patients. For calculating these statistical properties we use region growing and region merging techniques.

Keywords

Gray Level Cooccurrence Matrix (GLCM), best Matching Unit (BMU), Epoch Number, Idm (iInverse Difference Moment), Micro-Calcifications.
User
Notifications
Font Size