Medical Image Segmentation using Level Sets

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dc.contributor.authorImpoco, Gaetanoen_US
dc.contributor.editorVittorio Scarano and Rosario De Chiara and Ugo Erraen_US
dc.date.accessioned2014-01-27T16:30:11Z
dc.date.available2014-01-27T16:30:11Z
dc.date.issued2008en_US
dc.description.abstractComputer-aided diagnosis for pre-operative planning and post-operative outcome evaluation is widely considered an important topic for next-generation surgery. 3D models of the patients' anatomical structures can be highly valuable in this context. The accuracy of these models is strongly dependent on the classification and segmentation algorithms acting at the very first stage of the modelling chain. A promising class of segmentation algorithms is related to level set methods. Here, we briefly review some applications of level sets to medical image segmentation.en_US
dc.description.seriesinformationEurographics Italian Chapter Conferenceen_US
dc.identifier.isbn978-3-905673-68-5en_US
dc.identifier.urihttps://doi.org/10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2008/015-020en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.4.6 [Image Processing and Computer Vision]: Segmentation, Level Sets, 3D, MRI, CT, Medical Imagingen_US
dc.titleMedical Image Segmentation using Level Setsen_US
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