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dc.contributor.authorChen, CMen_US
dc.contributor.authorLu, HHSen_US
dc.date.accessioned2014-12-08T15:44:46Z-
dc.date.available2014-12-08T15:44:46Z-
dc.date.issued2000-10-01en_US
dc.identifier.issn0161-7346en_US
dc.identifier.urihttp://hdl.handle.net/11536/30223-
dc.description.abstractThe snake model is a widely-used approach to finding the boundary of the object of interest in an ultrasound image. However, due to the speckles, the weak edges and the tissue-related textures in an ultrasound image, conventional snake models usually cannot obtain the desired boundary satisfactorily. In this paper, we propose a new adaptive snake model for ultrasound image segmentation. The proposed snake model is composed of three major techniques, namely, the modified trimmed mean (MTM) filtering, ramp integration and adaptive weighting parameters. With the advantages of the mean and median filters, the MIM filter is employed to alleviate the speckle interference in the segmentation process. The weak edge enhancement by ramp integration attempts to capture the slowly varying edges, which are hard to capture by conventional snake models. The adaptive weighting parameter allows weighting of each energy term to change adaptively during the deformation process. The proposed snake model has been verified on the phantom and clinical ultrasound images. The experimental results showed that the proposed snake model achieves a reasonable performance with an initial contour placed 10 to 20 pixels away from the desired boundary. The mean minimal distances from the derived boundary to the desired boundary have been shown to be less than 3.5 (for CNR greater than or equal to 0.5) and 2.5 pixels, respectively, for the phantom and ultrasound images.en_US
dc.language.isoen_USen_US
dc.subjectadaptive weighting parametersen_US
dc.subjectmodified trimmed mean filteren_US
dc.subjectramp integrationen_US
dc.subjectsnake modelen_US
dc.subjectultrasound image segmentationen_US
dc.titleAn adaptive snake model for ultrasound image segmentation: Modified trimmed mean filter, ramp integration and adaptive weighting parametersen_US
dc.typeArticleen_US
dc.identifier.journalULTRASONIC IMAGINGen_US
dc.citation.volume22en_US
dc.citation.issue4en_US
dc.citation.spage214en_US
dc.citation.epage236en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000168739700003-
dc.citation.woscount10-
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