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dc.contributor.authorAppia, Vikram V.
dc.contributor.authorGanapathy, Balaji
dc.contributor.authorAbufadel, Amer Y.
dc.contributor.authorYezzi, Anthony
dc.contributor.authorFaber, Tracy
dc.date.accessioned2015-01-27T14:54:41Z
dc.date.available2015-01-27T14:54:41Z
dc.date.issued2010
dc.identifier.citationVikram V. Appia; Balaji Ganapathy; Amer Abufadel; Anthony Yezzi and Tracy Faber, "A regions of confidence based approach to enhance segmentation with shape priors", Proc. SPIE 7533, Computational Imaging VIII, 753302 (2010).en_US
dc.identifier.urihttp://hdl.handle.net/1853/53155
dc.description©2010 SPIE - Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.en_US
dc.descriptionPresented at Computational Imaging VIII, January 17, 2010, San Jose, CA.
dc.descriptionhttp://dx.doi.org/10.1117/12.850888
dc.description.abstractWe propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest. Since, all the training images are acquired under similar conditions, we can train our algorithm to estimate these regions as well. Based on this training we will generate a map which indicates the regions in the image that are likely to contain no useful information for segmentation. We then use a parametric model to represent the segmenting curve as a combination of shape priors obtained by representing the training data as a collection of signed distance functions. We evolve an objective energy functional to evolve the global parameters that are used to represent the curve. We vary the influence each pixel has on the evolution of these parameters based on the confidence/interest label. When we use these labels to indicate the regions with low confidence; the regions containing accurate edges will have a dominant role in the evolution of the curve and the segmentation in the low confidence regions will be approximated based on the training data. Since our model evolves global parameters, it improves the segmentation even in the regions with accurate edges. This is because we eliminate the influence of the low confidence regions which may mislead the final segmentation. Similarly when we use the labels to indicate the regions which are not of importance, we will get a better segmentation of the object in the regions we are interested in.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectActive contoursen_US
dc.subjectCardiac MRI segmentationen_US
dc.subjectCurve evolutionen_US
dc.subjectEigenshapesen_US
dc.subjectImage alignmenten_US
dc.subjectPrincipal component analysisen_US
dc.subjectRegions of confidence labelsen_US
dc.subjectRegions of interest labelsen_US
dc.subjectShape alignmenten_US
dc.subjectShape priorsen_US
dc.titleA Regions of Confidence Based Approach to Enhance Segmentation with Shape Priorsen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Laboratory of Computational Computer Visionen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.contributor.corporatenameKhoury Groupen_US
dc.contributor.corporatenameEmory Universityen_US
dc.publisher.originalSociety of Photo-Optical Instrumentation Engineers
dc.identifier.doi10.1117/12.850888
dc.embargo.termsnullen_US


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