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dc.contributor.authorKarsch, Kevineng
dc.contributor.authorHe, Qingeng
dc.contributor.authorDuan, Yeeng
dc.contributor.authorMiles, Judith H.eng
dc.contributor.corporatenameUniversity of Missouri-Columbia. Office of Undergraduate Researcheng
dc.contributor.meetingnameUndergraduate Research and Creative Achievements Forum (2008 : University of Missouri--Columbia)eng
dc.date2008eng
dc.date.issued2008eng
dc.descriptionAbstract only availableeng
dc.description.abstractOn a daily basis, numerous medical institutions use MRI segmentation to conduct research and treat patients. While there are many different segmentation algorithms that exist today, most have drawbacks in respect to either efficiency or accuracy. We present an algorithm that utilizes our unique seeding procedure and integrates several of these methods to achieve robust semiautomatic segmentation on MRI that can be done efficiently on modern CPUs. The seeding method is characterized by a progression of k-Means clustering, connected components searches, and mathematical morphology iterations in order to achieve a seed that closely resembles the target shape to be segmented. The seed is then translated into a level set equation and evolved according to a hybrid k-Means/level set force equation until the target shape is reached. Due to our seeding method, this deformation suffers from far less local minima than had we used a generic seeding procedure. Also, because the seed is near the target shape, the computational complexity is drastically reduced while evolving the level set to its steady state. The entire process requires a single mouse click from a user inside the target region and is done on one slice of an MRI scan in two dimensions.eng
dc.description.sponsorshipCollege of Engineering Undergraduate Research Optioneng
dc.identifier.urihttp://hdl.handle.net/10355/1927eng
dc.languageen_USeng
dc.publisherUniversity of Missouri--Columbia. Office of Undergraduate Researcheng
dc.relation.ispartof2008 Undergraduate Research and Creative Achievements Forum (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Office of Undergraduate Research. Undergraduate Research and Creative Achievements Forumeng
dc.subjectseeding procedureeng
dc.subjectk-Means clusteringeng
dc.subjectcomputational complexityeng
dc.titleA novel algorithm for semiautomatic brain structure segmentation from MRI [abstract]eng
dc.typePresentationeng


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