Computer-aided diagnosis in high resolution CT of the lungs
A computer-aided diagnosis (CAD) system is presented to automatically distinguish normal from abnormal tissue in high-resolution CT chest scans acquired during daily clinical practice. From high-resolution computed tomography scans of 116 patients, 657 regions of interest are extracted that are to b...
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| Veröffentlicht in: | Medical physics (Lancaster) 2003-12, Vol.30 (12), p.3081-3090 |
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| container_title | Medical physics (Lancaster) |
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| creator | Sluimer, Ingrid C. van Waes, Paul F. Viergever, Max A. van Ginneken, Bram |
| description | A computer-aided diagnosis (CAD) system is presented to automatically distinguish normal from abnormal tissue in high-resolution CT chest scans acquired during daily clinical practice. From high-resolution computed tomography scans of 116 patients, 657 regions of interest are extracted that are to be classified as displaying either normal or abnormal lung tissue. A principled texture analysis approach is used, extracting features to describe local image structure by means of a multi-scale filter bank. The use of various classifiers and feature subsets is compared and results are evaluated with ROC analysis. Performance of the system is shown to approach that of two expert radiologists in diagnosing the local regions of interest, with an area under the ROC curve of 0.862 for the CAD scheme versus 0.877 and 0.893 for the radiologists. |
| doi_str_mv | 10.1118/1.1624771 |
| format | Article |
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From high-resolution computed tomography scans of 116 patients, 657 regions of interest are extracted that are to be classified as displaying either normal or abnormal lung tissue. A principled texture analysis approach is used, extracting features to describe local image structure by means of a multi-scale filter bank. The use of various classifiers and feature subsets is compared and results are evaluated with ROC analysis. 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From high-resolution computed tomography scans of 116 patients, 657 regions of interest are extracted that are to be classified as displaying either normal or abnormal lung tissue. A principled texture analysis approach is used, extracting features to describe local image structure by means of a multi-scale filter bank. The use of various classifiers and feature subsets is compared and results are evaluated with ROC analysis. Performance of the system is shown to approach that of two expert radiologists in diagnosing the local regions of interest, with an area under the ROC curve of 0.862 for the CAD scheme versus 0.877 and 0.893 for the radiologists.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computed radiography</subject><subject>Computed tomography</subject><subject>Computer aided diagnosis</subject><subject>computerised tomography</subject><subject>feature extraction</subject><subject>high‐resolution CT</subject><subject>Humans</subject><subject>Image analysis</subject><subject>image classification</subject><subject>image texture</subject><subject>lung</subject><subject>Lung - diagnostic imaging</subject><subject>Lung Diseases - diagnostic imaging</subject><subject>Lungs</subject><subject>medical image processing</subject><subject>Medical imaging</subject><subject>Multiscale methods</subject><subject>pattern recognition</subject><subject>Pattern Recognition, Automated</subject><subject>Physicists</subject><subject>Radiographic Image Enhancement - methods</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiologists</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>texture analysis</subject><subject>Tissues</subject><subject>vectors</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90EtLxDAUBeAgijOOLvwDkpWg0DG3TfrAlRRfoOhiXJc0j5lI24xJq8y_t0OLCqKrs_nu4XIQOgYyB4D0AuYQhzRJYAdN-4wCGpJsF00JyWgQUsIm6MD7V0JIHDGyjyZAE4hIQqfoMrf1umuVC7iRSmJp-LKx3nhsGrwyyxV2ytuqa41tcL7AVuN2pXDVNUt_iPY0r7w6GnOGXm6uF_ld8PB0e59fPQSCphQCoYkseRQrYBAzVgopQwlMZ6AY0zTJIKaKgMhCIbI00iJVSUw5pEykZUIgmqHToXft7FunfFvUxgtVVbxRtvNFAjSLaRb28GyAwlnvndLF2pmau00BpNguVUAxLtXbk7G0K2slv-U4TQ-CAXyYSm3-bioen8fC88F7YVq-3evr5t26H34t9X_496ufoxyLLw</recordid><startdate>200312</startdate><enddate>200312</enddate><creator>Sluimer, Ingrid C.</creator><creator>van Waes, Paul F.</creator><creator>Viergever, Max A.</creator><creator>van Ginneken, Bram</creator><general>American Association of Physicists in Medicine</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>200312</creationdate><title>Computer-aided diagnosis in high resolution CT of the lungs</title><author>Sluimer, Ingrid C. ; van Waes, Paul F. ; Viergever, Max A. ; van Ginneken, Bram</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4841-cf0dba36e151655bcdd2d15f91e55f479164e01c92cc983fc8e764a185c8b7013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Computed radiography</topic><topic>Computed tomography</topic><topic>Computer aided diagnosis</topic><topic>computerised tomography</topic><topic>feature extraction</topic><topic>high‐resolution CT</topic><topic>Humans</topic><topic>Image analysis</topic><topic>image classification</topic><topic>image texture</topic><topic>lung</topic><topic>Lung - diagnostic imaging</topic><topic>Lung Diseases - diagnostic imaging</topic><topic>Lungs</topic><topic>medical image processing</topic><topic>Medical imaging</topic><topic>Multiscale methods</topic><topic>pattern recognition</topic><topic>Pattern Recognition, Automated</topic><topic>Physicists</topic><topic>Radiographic Image Enhancement - methods</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Radiologists</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>texture analysis</topic><topic>Tissues</topic><topic>vectors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sluimer, Ingrid C.</creatorcontrib><creatorcontrib>van Waes, Paul F.</creatorcontrib><creatorcontrib>Viergever, Max A.</creatorcontrib><creatorcontrib>van Ginneken, Bram</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sluimer, Ingrid C.</au><au>van Waes, Paul F.</au><au>Viergever, Max A.</au><au>van Ginneken, Bram</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computer-aided diagnosis in high resolution CT of the lungs</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2003-12</date><risdate>2003</risdate><volume>30</volume><issue>12</issue><spage>3081</spage><epage>3090</epage><pages>3081-3090</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><coden>MPHYA6</coden><abstract>A computer-aided diagnosis (CAD) system is presented to automatically distinguish normal from abnormal tissue in high-resolution CT chest scans acquired during daily clinical practice. From high-resolution computed tomography scans of 116 patients, 657 regions of interest are extracted that are to be classified as displaying either normal or abnormal lung tissue. A principled texture analysis approach is used, extracting features to describe local image structure by means of a multi-scale filter bank. The use of various classifiers and feature subsets is compared and results are evaluated with ROC analysis. Performance of the system is shown to approach that of two expert radiologists in diagnosing the local regions of interest, with an area under the ROC curve of 0.862 for the CAD scheme versus 0.877 and 0.893 for the radiologists.</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><pmid>14713074</pmid><doi>10.1118/1.1624771</doi><tpages>10</tpages></addata></record> |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Artificial Intelligence Computed radiography Computed tomography Computer aided diagnosis computerised tomography feature extraction high‐resolution CT Humans Image analysis image classification image texture lung Lung - diagnostic imaging Lung Diseases - diagnostic imaging Lungs medical image processing Medical imaging Multiscale methods pattern recognition Pattern Recognition, Automated Physicists Radiographic Image Enhancement - methods Radiographic Image Interpretation, Computer-Assisted - methods Radiologists Reproducibility of Results Sensitivity and Specificity texture analysis Tissues vectors |
| title | Computer-aided diagnosis in high resolution CT of the lungs |
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