Name
Title | ||
|---|---|---|
Computerized detection of masses in digital mammograms: Investigation of feature-analysis techniques |
Abstract | ||
|---|---|---|
Mammographic screening of asymptomatic women has shown effectiveness in the reduction of breast cancer mortality. We are developing
a computerized scheme for the detection of mammographic masses as an aid to radiologists in mammographic screening programs.
Possible masses on digitized screen/film mammograms are initially identified using a nonlinear bilateral-subtraction technique,
which is based on asymmetric density patterns occurring in corresponding portions of right and left mammograms. In this study,
we analyze the characteristics of actual masses and nonmass detections to develop feature-analysis techniques with which to
reduce the number of non-mass (ie, false-positive) detections. These feature-analysis techniques involve (1) the extraction
of various features (such as area, contrast, circularity and border-distance based on the density and geometric information
of masses in both processed, and original breast images), and (2) tests of the extracted features to reduce nonmass detections.
Cumulative histograms of both actual-mass detections and nonmass detections are used to characterize extracted features and
to determine the cutoff values used in the feature tests. The effectiveness of the feature-analysis techniques is evaluated
in combination with the computerized detection scheme that uses the nonlinear bilateral-subtraction technique using free-response
receiver operating characteristic analysis and 77 patient cases (308 mammograms). Results show that the feature-analysis techniques
effectively improve the performance of the computerized detection scheme: about 35% false-positive detections were eliminated
without loss in sensitivity when the feature-analysis techniques were used. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1007/BF03168475 | Journal of Digital Imaging |
Keywords | Field | DocType |
digital mammography, computer-vision, free-response receiver operating characteristic (FROC) analysis, feature analysis, computer-aided diagnosis | Digital mammography,Mammography,Histogram,Computer vision,Computer science,Receiver operating characteristic analysis,Computer-aided diagnosis,Cutoff,Artificial intelligence,Pattern recognition (psychology),Radiographic Image Enhancement | Journal |
Volume | Issue | ISSN |
7 | 1 | 0897-1889 |
Citations | PageRank | References |
8 | 2.04 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang-Fang Yin | 1 | 14 | 5.24 |
| Maryellen L. Giger | 2 | 393 | 85.89 |
| Kunio Doi | 3 | 8 | 2.04 |
| C J Vyborny | 4 | 43 | 8.64 |
| robert a schmidt | 5 | 25 | 8.23 |