Digital Mammography Research at Purdue University
Breast cancer is one of the leading causes of death in women. Studies
have indicated that cure rates dramatically increase if the breast lesion
can be detected at a size less than 1 centimeter, which is too small for
the lesion to be palpable. The only way a lesion this small can be
detected is through screening mammography. Our goal
has been to develop computer-aided diagnostic techniques for automatic detection
and classification of breast tumors.
This research is led by Prof. Edward
J. Delp at the Video and
Image Processing Laboratory (VIPER).
Features of Breast Abnormalities in Mammograms
Usually breast abnormalities are characterized into three classes:
In the past several years there has been tremendous interest in image processing
and analysis techniques in mammography. One common approach for detecting
abnormalities in mammograms is to use a series of heuristics, e.g. filtering
and thresholding, which may include texture analysis to automatically detect
abnormalities. These heuristic methods suffer from a lack of robustness
when the number of images to be classified is large. Statistical
methods have also been developed to address this problem.
Our
new statistical algorithm partitions a mammogram into homogeneous texture
regions using random field model.
M. L. Comer, S. Liu, E. J. Delp, "Statistical Segmentation of Mammograms,"
Proceedings of the 3nd International Workshop on Digital Mammography,
June 9-12,1996, Chicago, pp. 475-478.
The
readme file ,
compressed postscript file,
PDF file, and the
ftp site.
Among breast abnormalities, spiculated masses having a stellate appearance
in mammograms are highly suspicious indicators of breast cancer.
Unfortunately, the detection of spiculated lesions is very difficult.
Their central masses are usually irregular with ill-defined borders.
Their sizes vary from a few millimeters to several centimeters in diameter.
We have proposed a
new multiresolution scheme for the detection of spiculated lesions in
digital mammograms.
S. Liu, C. F. Babbs, and E. J. Delp
"Multiresolution Detection of Spiculated Lesions in Digital Mammograms,"
submitted to the IEEE Transactions on Image Processing,
The
readme
file,
compressed postscript file,
PDF file, and the
ftp site.
S. Liu and E. J. Delp, "Multiresolution Detection of Stellate Lesions
in Mammograms,"
Proceedings of IEEE International Conference on Image Processing,
October 26-29,1997, Santa Barbara, California, pp. II-109-II-112.
The
readme
file,
compressed postscript file,
PDF file, and the
ftp site.
Identification of Normal Digital Mammograms
We propose 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.
The block diagram at right shows the normal tissue identification and removal
algorithm. We will refer to the mammogram that results from the removal of
normal structures as the residual image.
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.
Eventually, the computer-aided diagnostic software would prescreen mammograms
with quantifiable high
accuracy and presents to the human reader a reduced number of more difficult
cases, together with the residual images. Hence it gives the human reader
both a reduced work load and extra clues or prompts, which would
improve his or her overall performance.
S. Liu, C. F. Babbs, and E. J. Delp,
"Normal Mammogram Analysis and Recognition,"
Proceedings of the IEEE International Conference on Image Processing,
October 4-7,1998, Chicago, Illinois. The
readme
file,
compressed postscript file,
PDF file, and the
ftp site.
Research in the processing, compression, transmission, and interpretation
of digital radiographic images requires evaluation of a wide variety of
test images, varying in format, in spatial resolution, and in anatomic
content. To evaluate the diagnostic performance of observers using
novel versus conventional image formats, large number of test images containing
known abnormalities are required. Generating synthetic
mammograms provide a flexible, easy-to-use research tool to explore
digital techniques in mammography, as well as a potential aid to training
of radiologists in early breast cancer detection.
Publications
Our recent publications in
Medical Imaging and
Image Analysis.
A complete set of
recent publications at
VIPER.
Useful Resources
Digital Mammography Research at Purdue University - Professor Edward J. Delp