Pectoral muscle identification in mammograms
In most of the approaches of computer‐aided detection of breast cancer, one of the preprocessing steps applied to the mammogram is the removal/suppression of pectoral muscle, as its presence within the mammogram may adversely affect the outcome of cancer detection processes. Through this study, we p...
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| Veröffentlicht in: | Journal of applied clinical medical physics 2011-03, Vol.12 (3), p.215-230 |
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| description | In most of the approaches of computer‐aided detection of breast cancer, one of the preprocessing steps applied to the mammogram is the removal/suppression of pectoral muscle, as its presence within the mammogram may adversely affect the outcome of cancer detection processes. Through this study, we propose an efficient automatic method using the watershed transformation for identifying the pectoral muscle in mediolateral oblique view mammograms. The watershed transformation of the mammogram shows interesting properties that include the appearance of a unique watershed line corresponding to the pectoral muscle edge. In addition to this, it is observed that the pectoral muscle region is oversegmented due to the existence of several catchment basins within the pectoral muscle. Hence, a suitable merging algorithm is proposed to combine the appropriate catchment basins to obtain the correct pectoral muscle region. A total of 84 mammograms from the mammographic image analysis database were used to validate this approach. The mean false positive and mean false negative rates, obtained by comparing the results of the proposed approach with manually‐identified (ground truth) pectoral muscle boundaries, respectively, were 0.85% and 4.88%. A comparison of the results of the proposed method with related state‐of‐the‐art methods shows that the performance of the proposed approach is better than the existing methods in terms of the mean false negative rate. Using Hausdorff distance metric, the comparison of the results of the proposed method with ground truth shows low Hausdorff distances, the mean and standard deviation being 3.85±1.07 mm.
PACS numbers: 87.57.R, 87.57.nm, 87.59.ej, 87.85.Ng, 87.85.Pq |
| doi_str_mv | 10.1120/jacmp.v12i3.3285 |
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PACS numbers: 87.57.R, 87.57.nm, 87.59.ej, 87.85.Ng, 87.85.Pq</description><identifier>ISSN: 1526-9914</identifier><identifier>EISSN: 1526-9914</identifier><identifier>DOI: 10.1120/jacmp.v12i3.3285</identifier><identifier>PMID: 21844845</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Accuracy ; Algorithms ; biomedical image analysis ; Boundaries ; Breast cancer ; Breast Neoplasms - diagnostic imaging ; Breast Neoplasms - pathology ; computer‐aided detection ; Databases, Factual ; Datasets ; False Negative Reactions ; False Positive Reactions ; Female ; Humans ; Identification ; mammogram analysis ; Mammography ; Mammography - methods ; Medical Imaging ; Methods ; Pattern Recognition, Automated - methods ; pectoral muscle identification ; Pectoralis Muscles - diagnostic imaging ; Pectoralis Muscles - pathology ; Radiographic Image Interpretation, Computer-Assisted - methods ; Reproducibility of Results ; segmentation ; Sensitivity and Specificity ; Standard deviation ; Watersheds</subject><ispartof>Journal of applied clinical medical physics, 2011-03, Vol.12 (3), p.215-230</ispartof><rights>2011 The Authors.</rights><rights>2011. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5315-74165d4fc6bdc0199d5f279ec0b63d4e420e037a437d905fdebb2630b55dc3103</citedby><cites>FETCH-LOGICAL-c5315-74165d4fc6bdc0199d5f279ec0b63d4e420e037a437d905fdebb2630b55dc3103</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718641/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718641/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,1417,11562,27924,27925,45574,45575,46052,46476,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21844845$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Camilus, K. Santle</creatorcontrib><creatorcontrib>Govindan, V. K.</creatorcontrib><creatorcontrib>Sathidevi, P.S.</creatorcontrib><title>Pectoral muscle identification in mammograms</title><title>Journal of applied clinical medical physics</title><addtitle>J Appl Clin Med Phys</addtitle><description>In most of the approaches of computer‐aided detection of breast cancer, one of the preprocessing steps applied to the mammogram is the removal/suppression of pectoral muscle, as its presence within the mammogram may adversely affect the outcome of cancer detection processes. Through this study, we propose an efficient automatic method using the watershed transformation for identifying the pectoral muscle in mediolateral oblique view mammograms. The watershed transformation of the mammogram shows interesting properties that include the appearance of a unique watershed line corresponding to the pectoral muscle edge. In addition to this, it is observed that the pectoral muscle region is oversegmented due to the existence of several catchment basins within the pectoral muscle. Hence, a suitable merging algorithm is proposed to combine the appropriate catchment basins to obtain the correct pectoral muscle region. A total of 84 mammograms from the mammographic image analysis database were used to validate this approach. The mean false positive and mean false negative rates, obtained by comparing the results of the proposed approach with manually‐identified (ground truth) pectoral muscle boundaries, respectively, were 0.85% and 4.88%. A comparison of the results of the proposed method with related state‐of‐the‐art methods shows that the performance of the proposed approach is better than the existing methods in terms of the mean false negative rate. Using Hausdorff distance metric, the comparison of the results of the proposed method with ground truth shows low Hausdorff distances, the mean and standard deviation being 3.85±1.07 mm.
PACS numbers: 87.57.R, 87.57.nm, 87.59.ej, 87.85.Ng, 87.85.Pq</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>biomedical image analysis</subject><subject>Boundaries</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - diagnostic imaging</subject><subject>Breast Neoplasms - pathology</subject><subject>computer‐aided detection</subject><subject>Databases, Factual</subject><subject>Datasets</subject><subject>False Negative Reactions</subject><subject>False Positive Reactions</subject><subject>Female</subject><subject>Humans</subject><subject>Identification</subject><subject>mammogram analysis</subject><subject>Mammography</subject><subject>Mammography - methods</subject><subject>Medical Imaging</subject><subject>Methods</subject><subject>Pattern Recognition, Automated - methods</subject><subject>pectoral muscle identification</subject><subject>Pectoralis Muscles - diagnostic imaging</subject><subject>Pectoralis Muscles - pathology</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Reproducibility of Results</subject><subject>segmentation</subject><subject>Sensitivity and Specificity</subject><subject>Standard deviation</subject><subject>Watersheds</subject><issn>1526-9914</issn><issn>1526-9914</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkctLAzEQxoMotlbvnqTgwYutmTx2sxdBii-o6EHPIZtka8pmUzddpf-924elevE0A_Obj2_mQ-gU8BCA4Kup0n42_ATi6JASwfdQFzhJBlkGbH-n76CjGKcYAwgqDlGHgGBMMN5Fly9Wz0Otyr5voi5t3xlbzV3htJq7UPVd1ffK-zCplY_H6KBQZbQnm9pDb3e3r6OHwfj5_nF0Mx5oToEPUgYJN6zQSW40hiwzvCBpZjXOE2qYZQRbTFPFaGoyzAtj85wkFOecG00B0x66XuvOmtxbo1tHrUM5q51X9UIG5eTvSeXe5SR8Sp6CSBi0AhcbgTp8NDbOpXdR27JUlQ1NlEIwIJSsyPM_5DQ0ddVeJwkRmcCpSEhL4TWl6xBjbYutF8BymYRcJSFXSchlEu3K2e4N24Wf17dAsga-XGkX_wrKm9ETwQQ4_QYrK5bB</recordid><startdate>20110303</startdate><enddate>20110303</enddate><creator>Camilus, K. Santle</creator><creator>Govindan, V. K.</creator><creator>Sathidevi, P.S.</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</scope><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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88I</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0S</scope><scope>M2P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20110303</creationdate><title>Pectoral muscle identification in mammograms</title><author>Camilus, K. Santle ; Govindan, V. K. ; Sathidevi, P.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5315-74165d4fc6bdc0199d5f279ec0b63d4e420e037a437d905fdebb2630b55dc3103</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>biomedical image analysis</topic><topic>Boundaries</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - diagnostic imaging</topic><topic>Breast Neoplasms - pathology</topic><topic>computer‐aided detection</topic><topic>Databases, Factual</topic><topic>Datasets</topic><topic>False Negative Reactions</topic><topic>False Positive Reactions</topic><topic>Female</topic><topic>Humans</topic><topic>Identification</topic><topic>mammogram analysis</topic><topic>Mammography</topic><topic>Mammography - methods</topic><topic>Medical Imaging</topic><topic>Methods</topic><topic>Pattern Recognition, Automated - methods</topic><topic>pectoral muscle identification</topic><topic>Pectoralis Muscles - diagnostic imaging</topic><topic>Pectoralis Muscles - pathology</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Reproducibility of Results</topic><topic>segmentation</topic><topic>Sensitivity and Specificity</topic><topic>Standard deviation</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Camilus, K. 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Santle</au><au>Govindan, V. K.</au><au>Sathidevi, P.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pectoral muscle identification in mammograms</atitle><jtitle>Journal of applied clinical medical physics</jtitle><addtitle>J Appl Clin Med Phys</addtitle><date>2011-03-03</date><risdate>2011</risdate><volume>12</volume><issue>3</issue><spage>215</spage><epage>230</epage><pages>215-230</pages><issn>1526-9914</issn><eissn>1526-9914</eissn><abstract>In most of the approaches of computer‐aided detection of breast cancer, one of the preprocessing steps applied to the mammogram is the removal/suppression of pectoral muscle, as its presence within the mammogram may adversely affect the outcome of cancer detection processes. Through this study, we propose an efficient automatic method using the watershed transformation for identifying the pectoral muscle in mediolateral oblique view mammograms. The watershed transformation of the mammogram shows interesting properties that include the appearance of a unique watershed line corresponding to the pectoral muscle edge. In addition to this, it is observed that the pectoral muscle region is oversegmented due to the existence of several catchment basins within the pectoral muscle. Hence, a suitable merging algorithm is proposed to combine the appropriate catchment basins to obtain the correct pectoral muscle region. A total of 84 mammograms from the mammographic image analysis database were used to validate this approach. The mean false positive and mean false negative rates, obtained by comparing the results of the proposed approach with manually‐identified (ground truth) pectoral muscle boundaries, respectively, were 0.85% and 4.88%. A comparison of the results of the proposed method with related state‐of‐the‐art methods shows that the performance of the proposed approach is better than the existing methods in terms of the mean false negative rate. Using Hausdorff distance metric, the comparison of the results of the proposed method with ground truth shows low Hausdorff distances, the mean and standard deviation being 3.85±1.07 mm.
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| subjects | Accuracy Algorithms biomedical image analysis Boundaries Breast cancer Breast Neoplasms - diagnostic imaging Breast Neoplasms - pathology computer‐aided detection Databases, Factual Datasets False Negative Reactions False Positive Reactions Female Humans Identification mammogram analysis Mammography Mammography - methods Medical Imaging Methods Pattern Recognition, Automated - methods pectoral muscle identification Pectoralis Muscles - diagnostic imaging Pectoralis Muscles - pathology Radiographic Image Interpretation, Computer-Assisted - methods Reproducibility of Results segmentation Sensitivity and Specificity Standard deviation Watersheds |
| title | Pectoral muscle identification in mammograms |
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