{"version":1,"kind":"Article","sha256":"","slug":"597","location":"","dependencies":[],"doi":"10.54294/1h2wu4","frontmatter":{"title":"Efficient Automated Detection and Segmentation of Medium and Large Liver Tumors: CAD Approach","abstract":"In this paper, we present a fully automated system that detects\r\nand segments potential liver cancer tumors from a thin slice CT\r\ndata. The system is targeted toward a tumor whose volume is\r\nlarger than $1 cm^3$, and is efficient as the average\r\ncomputation time per volume in our experiment is roughly 3.5\r\nminutes.\r\n\r\nThe system first reduces the volume size by 4x4x4 to reduce the\r\ncomputation and memory requirements. It then detects candidate\r\nlocations as local minima of the intensity fields after a\r\nvariant of textit{elastic quadratic} smoothing. It then\r\nprovides a rough segmentation at each candidate by fitting a\r\nplane at sampled points near the periphery of the concave\r\nregion in the intensity profiles. The rough segmentation is\r\nused to estimate the range of intensity values in the tumor,\r\nwhich is used to obtain a more accurate segmentation by a\r\nmethod originally developed for pulmonary nodules. The result\r\nof the second segmentation is interpolated at the resolution of\r\nthe original data.\r\n\r\nThe development of the system is a part of the 2008 3D\r\nSegmentation in the Clinic: A Grand Challenge competition. Four\r\nCT volumes containing 10 tumors were used for the development\r\nof the algorithm. Additional six CT volumes containing 10\r\ntumors were used to test the segmentation performance.","license":"You are licensing your work to Kitware Inc. under the\nCreative Commons Attribution License Version 3.0.\n\nKitware Inc. agrees to the following:\n\nKitware is free\n * to copy, distribute, display, and perform the work\n * to make derivative works\n * to make commercial use of the work\n\nUnder the following conditions:\n\\\"by Attribution\\\" - Kitware must attribute the work in the manner specified by the author or licensor.\n\n * For any reuse or distribution, they must make clear to others the license terms of this work.\n * Any of these conditions can be waived if they get permission from the copyright holder.\n\nYour fair use and other rights are in no way affected by the above.\n\nThis is a human-readable summary of the Legal Code (the full license) available at\nhttp://creativecommons.org/licenses/by/3.0/legalcode","keywords":["computer-aided detection","smoothing","diffusion","classfication"],"authors":[{"name":"Kubota, Toshiro","email":"kubota@susqu.edu","affiliations":["Susquehanna University"],"corresponding":true}],"date_submitted":"2008-07-07T20:40:22Z","external_publication_id":597,"revision_cids":["bafkreigk467qe3cvdyyuvggfxxx5lthpo76ob4qvgtg7xo7ex47eqnyqoy"]},"mdast":{"type":"root"},"downloads":[{"url":"https://ipfs.desci.com/ipfs/bafkreihapyhv5maqewrcbzu2lquexh4amzzvotyog3skhge4jqtjgnx2ai","title":"root/reviews.md","filename":"reviews.md","extra":{"size_bytes":1216,"type":"file"}},{"url":"https://ipfs.desci.com/ipfs/bafkreicm5yrbtrztzujvgac26uxwahsnidwrs5yxvqegb7xhbndc2ybude","title":"root/insight-journal-metadata.json","filename":"insight-journal-metadata.json","extra":{"size_bytes":7120,"type":"file"}},{"url":"https://dweb.link/ipfs/bafkreigzubwrwlxlvy6a5b62mqlqamitgdegvj7z6jlg2wjma2midgugsu","title":"root/article.pdf","filename":"article.pdf","extra":{"size_bytes":509145,"type":"file"}}],"references":{"cite":{"order":["ref1","ref2","ref3","ref4","ref5","ref6","ref7"]},"data":{"ref1":{"label":"ref1","enumerator":"1","url":"https://doi.org/10.1109/iai.2004.1300971","html":"Snake-based liver lesion segmentation+In Southwest Symposium on Image Analysis and Interpretation+187+191+2004+C. Krishnamurthy+J.J. Rodriguez+R.J. Gillies"},"ref2":{"label":"ref2","enumerator":"2","html":"Robust segmentation of pulmonary nodules of various densities: from ground-glass opacities to solid nodules+submitted. 1+T. Kubota"},"ref3":{"label":"ref3","enumerator":"3","url":"https://doi.org/10.1007/11569541_33","html":"Estimating diameters of pulmonary nodules with competition-diffusion and robust ellipsoid fit+In Computer Vision for Biomedical Image Applications+4+334+2005+3+T. Kubota+K. Okada"},"ref4":{"label":"ref4","enumerator":"4","url":"https://doi.org/10.1109/icpr.2006.93","html":"A machine learning approach for locating boundaries of liver tumors in ct images+In International Conference on Pattern Recognition+400+403+2006+Y.Z. Li+S. Hara+K. Shimura."},"ref5":{"label":"ref5","enumerator":"5","url":"https://doi.org/10.1109/iembs.2005.1617181","html":"Liver tumor volume estimation by semi-automatic segmentation method+In 27th Annual International Conference of the Engineering in Medicine and Biology Society+3296+3299+2005+R. Lu+P. Marziliano+C. H. Thng"},"ref6":{"label":"ref6","enumerator":"6","url":"https://doi.org/10.1109/tpami.2005.84","html":"Salient closed boundary extraction with ratio contour+IEEE Trans. Pattern Analysis and Machine Intelligence+3+4+2+561+April 2005+S. Wang+T. Kubota+J.M. Siskind+J. Wang"},"ref7":{"label":"ref7","enumerator":"7","url":"https://doi.org/10.1109/cbms.2003.1212810","html":"Volumetry of hepatic metastases in computed tomography using the watershed and active contour algorithms+In 16th IEEE Symposium on Computer-Based Medical Systems+329+335+2003+J. Peter+David J. Yim+Foran"}}}}