Centerline-based colon segmentation for CT colonography
We have developed a fully automated algorithm for colon segmentation, centerline-based segmentation (CBS), which is faster than any of the previously presented segmentation algorithms, but also has high sensitivity as well as high specificity. The algorithm first thresholds a set of unprocessed CT s...
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| Veröffentlicht in: | Medical physics (Lancaster) 2005-08, Vol.32 (8), p.2665-2672 |
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| creator | Frimmel, Hans Näppi, J. Yoshida, H. |
| description | We have developed a fully automated algorithm for colon segmentation, centerline-based segmentation (CBS), which is faster than any of the previously presented segmentation algorithms, but also has high sensitivity as well as high specificity. The algorithm first thresholds a set of unprocessed CT slices. Outer air is removed, after which a bounding box is computed. A centerline is computed for all remaining regions in the thresholded volume, disregarding segments related to extracolonic structures. Centerline segments are connected, after which the anatomy-based removal of segments representing extracolonic structures occurs. Segments related to the remaining centerline are locally region grown, and the colonic wall is found by dilation. Shape-based interpolation provides an isotropic mask. For 38 CT datasets, CBS was compared with the knowledge-guided segmentation (KGS) algorithm for sensitivity and specificity. With use of a 1.5 GHz AMD Athlon-based PC, the average computation time for the segmentation was 14.8 s. The sensitivity was, on average, 96%, and the specificity was 99%. A total of 21% of the voxels segmented by KGS, of which 96% represented extracolonic structures and 4% represented the colon, were removed. |
| doi_str_mv | 10.1118/1.1990288 |
| format | Article |
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The algorithm first thresholds a set of unprocessed CT slices. Outer air is removed, after which a bounding box is computed. A centerline is computed for all remaining regions in the thresholded volume, disregarding segments related to extracolonic structures. Centerline segments are connected, after which the anatomy-based removal of segments representing extracolonic structures occurs. Segments related to the remaining centerline are locally region grown, and the colonic wall is found by dilation. Shape-based interpolation provides an isotropic mask. For 38 CT datasets, CBS was compared with the knowledge-guided segmentation (KGS) algorithm for sensitivity and specificity. With use of a 1.5 GHz AMD Athlon-based PC, the average computation time for the segmentation was 14.8 s. The sensitivity was, on average, 96%, and the specificity was 99%. 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The algorithm first thresholds a set of unprocessed CT slices. Outer air is removed, after which a bounding box is computed. A centerline is computed for all remaining regions in the thresholded volume, disregarding segments related to extracolonic structures. Centerline segments are connected, after which the anatomy-based removal of segments representing extracolonic structures occurs. Segments related to the remaining centerline are locally region grown, and the colonic wall is found by dilation. Shape-based interpolation provides an isotropic mask. For 38 CT datasets, CBS was compared with the knowledge-guided segmentation (KGS) algorithm for sensitivity and specificity. With use of a 1.5 GHz AMD Athlon-based PC, the average computation time for the segmentation was 14.8 s. The sensitivity was, on average, 96%, and the specificity was 99%. A total of 21% of the voxels segmented by KGS, of which 96% represented extracolonic structures and 4% represented the colon, were removed.</description><subject>AIR</subject><subject>ALGORITHMS</subject><subject>ANATOMY</subject><subject>Artificial Intelligence</subject><subject>cancer</subject><subject>centerline</subject><subject>Cluster Analysis</subject><subject>colon</subject><subject>Colon - diagnostic imaging</subject><subject>Colonography, Computed Tomographic - instrumentation</subject><subject>Colonography, Computed Tomographic - methods</subject><subject>Computed radiography</subject><subject>Computed tomography</subject><subject>Computer aided diagnosis</subject><subject>Computer software</subject><subject>computerised tomography</subject><subject>COMPUTERIZED TOMOGRAPHY</subject><subject>CT colonography</subject><subject>IMAGE PROCESSING</subject><subject>image segmentation</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>INTERPOLATION</subject><subject>LARGE INTESTINE</subject><subject>Lungs</subject><subject>medical image processing</subject><subject>Medical imaging</subject><subject>NEOPLASMS</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Phantoms, Imaging</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiologists</subject><subject>RADIOLOGY AND NUCLEAR MEDICINE</subject><subject>Reproducibility of Results</subject><subject>RESPIRATORS</subject><subject>segmentation</subject><subject>SENSITIVITY</subject><subject>Sensitivity and Specificity</subject><subject>SPECIFICITY</subject><subject>Tissues</subject><subject>tumours</subject><subject>Visualization</subject><issn>0094-2405</issn><issn>2473-4209</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkU9v1DAQxS1ERZfCgS-AVkJCApTicRz_uSBV20KRiuBQuFqOM9kaZeNgJ1T77XGbiHJpxWlkzW-eZ94j5AXQYwBQ7-EYtKZMqUdkxbgsC86ofkxWlGpeME6rQ_I0pZ-UUlFW9Ak5BAG6lFquiNxgP2LsfI9FbRM2axe60K8Tbne5Y0efH22I683l3AnbaIer_TNy0Nou4fOlHpHvH88uN-fFxddPnzcnF4UTtFIFcxoQSyk4Y03lSg611kqUtaw1NlwqBqpSuVDtFAeLTjMUtq2YyByV5RF5N-umaxym2gzR72zcm2C9OfU_TkyIWzNNBioJkOlXMx3S6E1yfkR35ULfoxsNo5IJJlWmXs_UEMOvCdNodj457DrbY5iSEXnBSkuewTcz6GJIKWL793ug5sZ5A2ZxPrMvF9Gp3mFzRy5WZ6CYgWvf4f5-JfPl2yL4Ybk8n3Gbw_0zdyGa2xBNjioLvP1vgYfg3yH-s93QtOUf06i6bA</recordid><startdate>200508</startdate><enddate>200508</enddate><creator>Frimmel, Hans</creator><creator>Näppi, J.</creator><creator>Yoshida, H.</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><scope>OTOTI</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>DF2</scope></search><sort><creationdate>200508</creationdate><title>Centerline-based colon segmentation for CT colonography</title><author>Frimmel, Hans ; Näppi, J. ; Yoshida, H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6058-2c91ee376422d5c341b99863b7b9ed4782185878209c841aec92e6af526998073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>AIR</topic><topic>ALGORITHMS</topic><topic>ANATOMY</topic><topic>Artificial Intelligence</topic><topic>cancer</topic><topic>centerline</topic><topic>Cluster Analysis</topic><topic>colon</topic><topic>Colon - diagnostic imaging</topic><topic>Colonography, Computed Tomographic - instrumentation</topic><topic>Colonography, Computed Tomographic - methods</topic><topic>Computed radiography</topic><topic>Computed tomography</topic><topic>Computer aided diagnosis</topic><topic>Computer software</topic><topic>computerised tomography</topic><topic>COMPUTERIZED TOMOGRAPHY</topic><topic>CT colonography</topic><topic>IMAGE PROCESSING</topic><topic>image segmentation</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>INTERPOLATION</topic><topic>LARGE INTESTINE</topic><topic>Lungs</topic><topic>medical image processing</topic><topic>Medical imaging</topic><topic>NEOPLASMS</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Phantoms, Imaging</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Radiologists</topic><topic>RADIOLOGY AND NUCLEAR MEDICINE</topic><topic>Reproducibility of Results</topic><topic>RESPIRATORS</topic><topic>segmentation</topic><topic>SENSITIVITY</topic><topic>Sensitivity and Specificity</topic><topic>SPECIFICITY</topic><topic>Tissues</topic><topic>tumours</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Frimmel, Hans</creatorcontrib><creatorcontrib>Näppi, J.</creatorcontrib><creatorcontrib>Yoshida, H.</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><collection>OSTI.GOV</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Uppsala universitet</collection><jtitle>Medical physics (Lancaster)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Frimmel, Hans</au><au>Näppi, J.</au><au>Yoshida, H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Centerline-based colon segmentation for CT colonography</atitle><jtitle>Medical physics (Lancaster)</jtitle><addtitle>Med Phys</addtitle><date>2005-08</date><risdate>2005</risdate><volume>32</volume><issue>8</issue><spage>2665</spage><epage>2672</epage><pages>2665-2672</pages><issn>0094-2405</issn><eissn>2473-4209</eissn><coden>MPHYA6</coden><abstract>We have developed a fully automated algorithm for colon segmentation, centerline-based segmentation (CBS), which is faster than any of the previously presented segmentation algorithms, but also has high sensitivity as well as high specificity. The algorithm first thresholds a set of unprocessed CT slices. Outer air is removed, after which a bounding box is computed. A centerline is computed for all remaining regions in the thresholded volume, disregarding segments related to extracolonic structures. Centerline segments are connected, after which the anatomy-based removal of segments representing extracolonic structures occurs. Segments related to the remaining centerline are locally region grown, and the colonic wall is found by dilation. Shape-based interpolation provides an isotropic mask. For 38 CT datasets, CBS was compared with the knowledge-guided segmentation (KGS) algorithm for sensitivity and specificity. With use of a 1.5 GHz AMD Athlon-based PC, the average computation time for the segmentation was 14.8 s. The sensitivity was, on average, 96%, and the specificity was 99%. A total of 21% of the voxels segmented by KGS, of which 96% represented extracolonic structures and 4% represented the colon, were removed.</abstract><cop>United States</cop><pub>American Association of Physicists in Medicine</pub><pmid>16193797</pmid><doi>10.1118/1.1990288</doi><tpages>8</tpages></addata></record> |
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| subjects | AIR ALGORITHMS ANATOMY Artificial Intelligence cancer centerline Cluster Analysis colon Colon - diagnostic imaging Colonography, Computed Tomographic - instrumentation Colonography, Computed Tomographic - methods Computed radiography Computed tomography Computer aided diagnosis Computer software computerised tomography COMPUTERIZED TOMOGRAPHY CT colonography IMAGE PROCESSING image segmentation Imaging, Three-Dimensional - methods INTERPOLATION LARGE INTESTINE Lungs medical image processing Medical imaging NEOPLASMS Pattern Recognition, Automated - methods Phantoms, Imaging Radiographic Image Interpretation, Computer-Assisted - methods Radiologists RADIOLOGY AND NUCLEAR MEDICINE Reproducibility of Results RESPIRATORS segmentation SENSITIVITY Sensitivity and Specificity SPECIFICITY Tissues tumours Visualization |
| title | Centerline-based colon segmentation for CT colonography |
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