Linked Life Data

9775365

Source:http://linkedlifedata.com/resource/pubmed/id/9775365

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
9
pubmed:dateCreated
1998-12-17
pubmed:abstractText
Computer-aided diagnosis (CAD) schemes have the potential of substantially increasing diagnostic accuracy in mammography by providing the advantages of having a second reader. Our laboratory has developed a CAD scheme for detecting clustered microcalcifications in digital mammograms that is being tested clinically at the University of Chicago Hospitals. Our CAD scheme contains a large number of parameters such as filter weights, threshold levels, and region of interest (ROI) sizes. The choice of these parameter values determines the overall performance of the system and thus must be carefully set. Unfortunately, when the number of parameters becomes large, it is very difficult to obtain the optimal performance, especially when the values of the parameters are correlated with each other. In this study, we address the problem of identifying the optimal overall performance by developing an automated method for the determination of the parameter values that maximize the performance of a mammographic CAD scheme. Our method utilizes a genetic algorithm to search through the possible parameter values, and provides the set of parameters that minimize a cost function which measures the performance of the scheme. Using a database of 89 digitized mammograms, our method demonstrated that the sensitivity of our CAD scheme can be increased from 80% to 87% at a false positive rate of 1.0 per image. We estimate the average performance of our CAD scheme on unknown cases by performing jackknife tests; this was previously not feasible when the parameters of the CAD scheme were determined in a nonautomated manner.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0094-2405
pubmed:author
pubmed:issnType
Print
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1613-20
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms.
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