Switch to
Predicate | Object |
---|---|
rdf:type | |
lifeskim:mentions | |
pubmed:issue |
2
|
pubmed:dateCreated |
1995-10-27
|
pubmed:abstractText |
To improve the performance of a computerized scheme for detection of clustered microcalcifications in digitized mammograms, causes of detected false-positive microcalcification signals were analyzed. The false positives were grouped into four categories, namely, microcalcification like noise patterns, artifacts, linear patterns, and others. In an edge-gradient analysis, local edge-gradient values at signal-perimeter pixels of detected microcalcification signals were determined to eliminate false positives that look like subtle microcalcifications or are due to artifacts. In a linear-pattern analysis, the degree of linearity for linear patterns was determined from local gradient values from a set of linear templates oriented in 16 different directions. Threshold values for the edge-gradient analysis and the linear-pattern analysis were determined using a training database of 39 mammograms. It was possible to eliminate 59% and 25%, respectively, of 91 detected false-positive clusters with loss of only 3% of true-positive clusters. The combination of the two methods further improved the scheme in eliminating a total of 73% of the false-positive clusters with loss of 3% of true-positive clusters. Using these thresholds, the two methods were evaluated on another database of 50 mammograms. 62%, 31%, and 80% of the false-positive clusters were eliminated with loss of 3% of true-positive clusters or less, in the edge-gradient analysis, the linear-pattern analysis, and the combination of the two methods, respectively. The edge-gradient analysis and the linear-pattern analysis can reduce the false-positive detection rate, while maintaining a high level of the sensitivity.
|
pubmed:grant | |
pubmed:language |
eng
|
pubmed:journal | |
pubmed:citationSubset |
IM
|
pubmed:status |
MEDLINE
|
pubmed:month |
Feb
|
pubmed:issn |
0094-2405
|
pubmed:author | |
pubmed:issnType |
Print
|
pubmed:volume |
22
|
pubmed:owner |
NLM
|
pubmed:authorsComplete |
Y
|
pubmed:pagination |
161-9
|
pubmed:dateRevised |
2007-11-14
|
pubmed:meshHeading |
pubmed-meshheading:7565347-Artifacts,
pubmed-meshheading:7565347-Breast Diseases,
pubmed-meshheading:7565347-Breast Neoplasms,
pubmed-meshheading:7565347-Calcinosis,
pubmed-meshheading:7565347-Databases, Factual,
pubmed-meshheading:7565347-False Positive Reactions,
pubmed-meshheading:7565347-Female,
pubmed-meshheading:7565347-Humans,
pubmed-meshheading:7565347-Image Interpretation, Computer-Assisted,
pubmed-meshheading:7565347-Mammography,
pubmed-meshheading:7565347-Mathematics,
pubmed-meshheading:7565347-Models, Theoretical
|
pubmed:year |
1995
|
pubmed:articleTitle |
Image feature analysis and computer-aided diagnosis in mammography: reduction of false-positive clustered microcalcifications using local edge-gradient analysis.
|
pubmed:affiliation |
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637, USA.
|
pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, P.H.S.,
Research Support, Non-U.S. Gov't
|