Source:http://linkedlifedata.com/resource/pubmed/id/11797942
| Subject | Predicate | Object | Context |
|---|---|---|---|
| pubmed-article:11797942 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:11797942 | lifeskim:mentions | umls-concept:C0817096 | lld:lifeskim |
| pubmed-article:11797942 | lifeskim:mentions | umls-concept:C0205147 | lld:lifeskim |
| pubmed-article:11797942 | lifeskim:mentions | umls-concept:C1306645 | lld:lifeskim |
| pubmed-article:11797942 | lifeskim:mentions | umls-concept:C0008902 | lld:lifeskim |
| pubmed-article:11797942 | pubmed:issue | 12 | lld:pubmed |
| pubmed-article:11797942 | pubmed:dateCreated | 2002-1-18 | lld:pubmed |
| pubmed-article:11797942 | pubmed:abstractText | We propose to investigate the use of subregion Hotelling observers (SRHOs) in conjunction with perceptrons for the computerized classification of suspicious regions in chest radiographs for being nodules requiring follow up. Previously, 239 regions of interest (ROIs), each containing a suspicious lesion with proven classification, were collected. We chose to investigate the use of SRHOs as part of a multilayer classifier to determine the presence of a nodule. Each SRHO incorporates information about signal, background, and noise correlation for classification. For this study, 225 separate Hotelling observers were set up in a grid across each ROI. Each separate observer discriminates an 8 by 8 pixel area. A round robin sampling scheme was used to generate the 225 features, where each feature is the output of the individual observers. These features were then rank ordered by the magnitude of the weights of a perceptron. Once rank ordered, subsets of increasing number of features were selected to be used in another perceptron. This perceptron was trained to minimize mean squared error and the output was a continuous variable representing the likelihood of the region being a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis and reported as the area under the curve (Az). The classifier was optimized by adding additional features until the Az declined. The optimized subset of observers then were combined using a third perceptron. A subset of 80 features was selected which gave an Az of 0.972. Additionally, at 98.6% sensitivity, the classifier had a specificity of 71.3% and increased the positive predictive value from 60.7% to 84.1 %. Preliminary results suggest that using SRHOs in combination with perceptrons can provide a successful classification scheme for pulmonary nodules. This approach could be incorporated into a larger computer aided detection system for decreasing false positives. | lld:pubmed |
| pubmed-article:11797942 | pubmed:language | eng | lld:pubmed |
| pubmed-article:11797942 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:11797942 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:11797942 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:11797942 | pubmed:month | Dec | lld:pubmed |
| pubmed-article:11797942 | pubmed:issn | 0094-2405 | lld:pubmed |
| pubmed-article:11797942 | pubmed:author | pubmed-author:RoJ SJS | lld:pubmed |
| pubmed-article:11797942 | pubmed:author | pubmed-author:FloydC ECEJr | lld:pubmed |
| pubmed-article:11797942 | pubmed:author | pubmed-author:BaydushA HAH | lld:pubmed |
| pubmed-article:11797942 | pubmed:author | pubmed-author:AbbeyC KCK | lld:pubmed |
| pubmed-article:11797942 | pubmed:author | pubmed-author:CatariousD... | lld:pubmed |
| pubmed-article:11797942 | pubmed:issnType | lld:pubmed | |
| pubmed-article:11797942 | pubmed:volume | 28 | lld:pubmed |
| pubmed-article:11797942 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:11797942 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:11797942 | pubmed:pagination | 2403-9 | lld:pubmed |
| pubmed-article:11797942 | pubmed:dateRevised | 2007-11-15 | lld:pubmed |
| pubmed-article:11797942 | pubmed:meshHeading | pubmed-meshheading:11797942... | lld:pubmed |
| pubmed-article:11797942 | pubmed:meshHeading | pubmed-meshheading:11797942... | lld:pubmed |
| pubmed-article:11797942 | pubmed:meshHeading | pubmed-meshheading:11797942... | lld:pubmed |
| pubmed-article:11797942 | pubmed:meshHeading | pubmed-meshheading:11797942... | lld:pubmed |
| pubmed-article:11797942 | pubmed:meshHeading | pubmed-meshheading:11797942... | lld:pubmed |
| pubmed-article:11797942 | pubmed:meshHeading | pubmed-meshheading:11797942... | lld:pubmed |
| pubmed-article:11797942 | pubmed:meshHeading | pubmed-meshheading:11797942... | lld:pubmed |
| pubmed-article:11797942 | pubmed:year | 2001 | lld:pubmed |
| pubmed-article:11797942 | pubmed:articleTitle | Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers. | lld:pubmed |
| pubmed-article:11797942 | pubmed:affiliation | Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710, USA. alan.baydush@duke.edu | lld:pubmed |
| pubmed-article:11797942 | pubmed:publicationType | Journal Article | lld:pubmed |