[-] Show simple item record

dc.contributor.authorKarsch, Kevineng
dc.contributor.authorHe, Qingeng
dc.contributor.authorDuan, Yeeng
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.abstractWe propose a new hippocampus segmentation method from MRI by integrating region-growing methods such as K-means clustering with PDE-based level set methods. Starting from a single point provided by the user, our algorithm will first automatically generate an initial seed contour that closely resembles the hippocampus. The seed will then deform based on dynamic level-set equations, and will stop to obtain the final 3D shape when the equilibrium of the PDE is reached. In comparison with other hippocampus algorithms, our method is very efficient; most segmentations can be completed in under one minute using inexpensive hardware. Based on our experiments, our new algorithm is relatively robust to image noise and can work well with low contrast images.eng
dc.description.sponsorshipCollege of Engineering Undergraduate Research Optioneng
dc.identifier.urihttp://hdl.handle.net/10355/1908eng
dc.publisherUniversity of Missouri--Columbia. Office of Undergraduate Researcheng
dc.relation.ispartof2008 Summer 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.subjecthippocampus segmentationeng
dc.subjectmedical imagingeng
dc.titleA hybrid MRI-based hippocampus segmentation algorithmeng
dc.typePresentationeng


Files in this item

[PDF]
[PDF]

This item appears in the following Collection(s)

[-] Show simple item record