Linked Life Data

16485756

Source:http://linkedlifedata.com/resource/pubmed/id/16485756

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2006-2-20
pubmed:abstractText
Mammography is considered the most effective method for early detection of breast cancers. However, it is difficult for radiologists to detect microcalcification clusters. Therefore, we have developed a computerized scheme for detecting early-stage microcalcification clusters in mammograms. We first developed a novel filter bank based on the concept of the Hessian matrix for classifying nodular structures and linear structures. The mammogram images were decomposed into several subimages for second difference at scales from 1 to 4 by this filter bank. The subimages for the nodular component (NC) and the subimages for the nodular and linear component (NLC) were then obtained from analysis of the Hessian matrix. Many regions of interest (ROIs) were selected from the mammogram image. In each ROI, eight features were determined from the subimages for NC at scales from 1 to 4 and the subimages for NLC at scales from 1 to 4. The Bayes discriminant function was employed for distinguishing among abnormal ROIs with a microcalcification cluster and two different types of normal ROIs without a microcalcification cluster. We evaluated the detection performance by using 600 mammograms. Our computerized scheme was shown to have the potential to detect microcalcification clusters with a clinically acceptable sensitivity and low false positives.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0018-9294
pubmed:author
pubmed:issnType
Print
pubmed:volume
53
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
273-83
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed-meshheading:16485756-Algorithms, pubmed-meshheading:16485756-Artificial Intelligence, pubmed-meshheading:16485756-Breast Diseases, pubmed-meshheading:16485756-Breast Neoplasms, pubmed-meshheading:16485756-Calcinosis, pubmed-meshheading:16485756-Cluster Analysis, pubmed-meshheading:16485756-Discriminant Analysis, pubmed-meshheading:16485756-Female, pubmed-meshheading:16485756-Humans, pubmed-meshheading:16485756-Mammography, pubmed-meshheading:16485756-Pattern Recognition, Automated, pubmed-meshheading:16485756-Precancerous Conditions, pubmed-meshheading:16485756-Radiographic Image Interpretation, Computer-Assisted, pubmed-meshheading:16485756-Reproducibility of Results, pubmed-meshheading:16485756-Retrospective Studies, pubmed-meshheading:16485756-Sensitivity and Specificity, pubmed-meshheading:16485756-Signal Processing, Computer-Assisted
pubmed:year
2006
pubmed:articleTitle
Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms.
pubmed:affiliation
Department of Radiology, Mie University School of Medicine, Tsu, Japan. nakayama@clin.medic.mie-u.ac.jp
pubmed:publicationType
Journal Article, Evaluation Studies