Subject: 9/15 The Analysis of Digital Mammograms -- Edward J. Delp, School of Electrical Engineering Purdue University

From: Florissa Colina <[email-masked]>
Date: Thu, 09 Sep 1999 08:30:00 -0700

Berkeley Multimedia, Graphics and Interfaces Seminar

The Analysis of Digital Mammograms

Wednesday, September 15, 1999, 
1:00-2:30 p.m. PDT
Fujitsu Seminar Room (405 Soda Hall) 

Edward J. Delp 
School of Electrical Engineering Purdue University  

Breast cancer is one of the leading causes of death in women. It is estimated that one in seven women will get breast cancer. Recent studies have indicated that cure rates dramatically increase if the breast lesion can be detected at a size less than 1 centimeter. This size is too small for the lesion to be palpable. The only way a lesion this small can be detected is through screening mammography. 

In the past several years there has been tremendous interest in the use of image processing and analysis techniques in screening mammography. The goal of this research has been to develop techniques for automatic detection and classification of breast tumors. In this talk we will present two approaches to the analysis of digital mammograms. 

First, we will present a new multiresolution scheme for the detection of stellate lesions in digital mammograms. A multiresolution representation of the original mammogram is obtained using a linear phase nonseparable 2-D wavelet transform. A set of features are then extracted at each resolution for every pixel. Detection is performed from the coarsest resolution to the finest resolution using binary tree classifiers. This top-down approach requires less computation by starting with the least amount of data and propagating detection results to finer resolutions. 

Next, we present a novel approach to the problem of computer aided analysis of digital mammograms for breast cancer detection: namely, the development of algorithms to recognize unequivocally normal mammograms. First, we eliminate amorphous "clouds" or "blobs" in mammograms produced by normal glandular tissue of varying density using local average subtraction. Then we identify and remove normal connective tissue markings based on a set of specially designed line detectors. Any abnormality that may exist in the mammogram is therefore enhanced in the residual image, which makes the decision regarding the normality of the mammogram much easier.

---------------

The seminar is broadcast live on the Internet Mbone and as a Real
Networks G2 broadcast.

You can connect to the live RealNetworks broadcast at:

http://media2.bmrc.berkeley.edu/bibs/schedule.cfm 
You'll see a link to cs298-5/real networks/live programs.  

You can also directly put in this url into the Real Player:

rtsp://media2.bmrc.berkeley.edu/encoder/cs298-5.rm

To do so you will need to have the "Real Player G2" installed, which is
available from:
   http://www.real.com/products/player/downloadrealplayer.html

To tune into the Internet MBone broadcast, look for the announcement in
your MBone session directory program ('sdr').  If you are not receiving
the announcement you may be able to receive the session by manually
configuring the client programs ('vic', and 'vat') with the session
addresses:

Video:	low bit rate: 	233.0.25.125/22334
      	high bit rate:	233.0.25.1/22334

Audio:			233.0.25.3/22446

You can get further information about this seminar, and access to
replays of previous seminars at the MIG Seminar web page:

http://bmrc.berkeley.edu/298/index.html

Sponsored by the Berkeley Multimedia Research Center 

____________________________________________

Florissa Colina
Berkeley Multimedia Research Center (BMRC)
http://bmrc.berkeley.edu/
626 Soda Hall #1776
University of California
Berkeley, CA 94720-1776
Phone: (510) 643-0800   FAX: (510) 642-5615  
Email: [email-masked]