An Automatic Approach for Bone Tumor Detection from Non-Standard CT Images
Image processing techniques are applied in many fields of science. This study aims to detect tumors in the foot and create 3D models via computed tomography (CT), as well as to produce biometric data. 1 039 CT images were obtained from a server. The parameters used were a collimation of 64 detectors...
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Published in: | Ingeniería e investigación 2023-08, Vol.43 (3), p.e90748-e90748 |
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description | Image processing techniques are applied in many fields of science. This study aims to detect tumors in the foot and create 3D models via computed tomography (CT), as well as to produce biometric data. 1 039 CT images were obtained from a server. The parameters used were a collimation of 64 detectors, a scanning thickness of 0,5-3 mm, and a pixel size of 512 x 512, with a radiometric resolution of the 16-bit gray levels. Noise reduction, segmentation, and morphological analysis were performed on CT scans to detect bone tumors. In addition, this study used digital image processing techniques to create a virtual three-dimensional (3D) model of bone tumors. The performance of our proposal was evaluated by analyzing the receptor operating characteristics (ROC). According to the results, the sensitivity, specificity, and precision in tumor detection were 0,96, 1, and 0,98%, respectively, with a 0,99% average F-measure. Radiologist reports were used for the sake of comparison. The proposed technique for detecting bone tumors of the foot via CT can help radiologists with its increased precision, sensitivity, specificity, and F-measure. This method could improve the diagnosis of foot and ankle tumors by allowing for the multidirectional quantification of abnormalities. |
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This study aims to detect tumors in the foot and create 3D models via computed tomography (CT), as well as to produce biometric data. 1 039 CT images were obtained from a server. The parameters used were a collimation of 64 detectors, a scanning thickness of 0,5-3 mm, and a pixel size of 512 x 512, with a radiometric resolution of the 16-bit gray levels. Noise reduction, segmentation, and morphological analysis were performed on CT scans to detect bone tumors. In addition, this study used digital image processing techniques to create a virtual three-dimensional (3D) model of bone tumors. The performance of our proposal was evaluated by analyzing the receptor operating characteristics (ROC). According to the results, the sensitivity, specificity, and precision in tumor detection were 0,96, 1, and 0,98%, respectively, with a 0,99% average F-measure. Radiologist reports were used for the sake of comparison. 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Any link to this document should be made using its official URL in Dialnet. 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This study aims to detect tumors in the foot and create 3D models via computed tomography (CT), as well as to produce biometric data. 1 039 CT images were obtained from a server. The parameters used were a collimation of 64 detectors, a scanning thickness of 0,5-3 mm, and a pixel size of 512 x 512, with a radiometric resolution of the 16-bit gray levels. Noise reduction, segmentation, and morphological analysis were performed on CT scans to detect bone tumors. In addition, this study used digital image processing techniques to create a virtual three-dimensional (3D) model of bone tumors. The performance of our proposal was evaluated by analyzing the receptor operating characteristics (ROC). According to the results, the sensitivity, specificity, and precision in tumor detection were 0,96, 1, and 0,98%, respectively, with a 0,99% average F-measure. Radiologist reports were used for the sake of comparison. 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This method could improve the diagnosis of foot and ankle tumors by allowing for the multidirectional quantification of abnormalities.</description><subject>algoritmo de crecimiento regional</subject><subject>bone tumor</subject><subject>medical image processing</subject><subject>procesamiento de imágenes médica</subject><subject>region growing algorithm</subject><subject>tumor óseo</subject><issn>0120-5609</issn><issn>2248-8723</issn><issn>2248-8723</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVkV2LEzEUhoMoWNb9Bd7kD0w9yZxJJuDNWHWtLCtovQ6n-ahZOknJTAX_vUO3Cnt1Xl54Hzg8jL0VsBYdonqX8mGd8u8wzemwNqCxf8FWUmLf9Fq2L9kKhISmU2Bes9tpSntApUFowBX7OmQ-nOcy0pwcH06nWsj94rFU_qHkwHfncYkfwxzcnErmsZaRP5Tc_Jgpe6qeb3Z8O9IhTG_Yq0jHKdxe7w37-fnTbvOluf92t90M941D0c5NG6UzhvaODKoYwz5oMMYo6fsoOxX6zvcKowRNhEFr6L30EiICdQ46am_Y9onrCz3aU00j1T-2ULKXotSDpbp8cwzWecSWjNLkNdI-GEEyoAMRYugN4MJ6f2UlOuYwP8ddu3NONZVHsmGyw_cdAAiFGqVY5u3T3NUyTTXE_3sB9iLHLnLsPzn2Iqf9C50zhiM</recordid><startdate>20230804</startdate><enddate>20230804</enddate><creator>Catal Reis, Hatice</creator><creator>Bayram, Bulent</creator><general>Universidad Nacional de Colombia</general><scope>AAYXX</scope><scope>CITATION</scope><scope>AGMXS</scope><scope>FKZ</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-2696-2446</orcidid><orcidid>https://orcid.org/0000-0002-4248-116X</orcidid></search><sort><creationdate>20230804</creationdate><title>An Automatic Approach for Bone Tumor Detection from Non-Standard CT Images</title><author>Catal Reis, Hatice ; Bayram, Bulent</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c413t-3f2c99abca946ffebe7099962d8f256e85d864f207aa4e7708d2d20f40a5c05a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>algoritmo de crecimiento regional</topic><topic>bone tumor</topic><topic>medical image processing</topic><topic>procesamiento de imágenes médica</topic><topic>region growing algorithm</topic><topic>tumor óseo</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Catal Reis, Hatice</creatorcontrib><creatorcontrib>Bayram, Bulent</creatorcontrib><collection>CrossRef</collection><collection>Dialnet (Open Access Full Text)</collection><collection>Dialnet</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Ingeniería e investigación</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Catal Reis, Hatice</au><au>Bayram, Bulent</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Automatic Approach for Bone Tumor Detection from Non-Standard CT Images</atitle><jtitle>Ingeniería e investigación</jtitle><date>2023-08-04</date><risdate>2023</risdate><volume>43</volume><issue>3</issue><spage>e90748</spage><epage>e90748</epage><pages>e90748-e90748</pages><issn>0120-5609</issn><issn>2248-8723</issn><eissn>2248-8723</eissn><abstract>Image processing techniques are applied in many fields of science. 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subjects | algoritmo de crecimiento regional bone tumor medical image processing procesamiento de imágenes médica region growing algorithm tumor óseo |
title | An Automatic Approach for Bone Tumor Detection from Non-Standard CT Images |
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