{"id":2216,"date":"2010-12-25T21:38:16","date_gmt":"2010-12-25T21:38:16","guid":{"rendered":"http:\/\/hgpu.org\/?p=2216"},"modified":"2010-12-25T21:38:16","modified_gmt":"2010-12-25T21:38:16","slug":"multi-domain-higher-order-level-set-scheme-for-3d-image-segmentation-on-the-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2216","title":{"rendered":"Multi-domain, Higher Order Level Set Scheme for 3D Image Segmentation on the GPU"},"content":{"rendered":"<p>Level set method based segmentation provides an efficient tool for topological and geometrical shape handling. Conventional level set surfaces are only C^0 continuous since the level set evolution involves linear interpolation to compute derivatives. Bajaj et al. present a higher order method to evaluate level set surfaces that are C^2 continuous, but are slow due to high computational burden. In this paper, we provide a higher order GPU based solver for fast and efficient segmentation of large volumetric images. We also extend the higher order method to multi-domain segmentation. Our streaming solver is efficient in memory usage.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Level set method based segmentation provides an efficient tool for topological and geometrical shape handling. Conventional level set surfaces are only C^0 continuous since the level set evolution involves linear interpolation to compute derivatives. Bajaj et al. present a higher order method to evaluate level set surfaces that are C^2 continuous, but are slow due [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[73,89,33,3],"tags":[1791,14,1786,20,202],"class_list":["post-2216","post","type-post","status-publish","format-standard","hentry","category-computer-vision","category-nvidia-cuda","category-image-processing","category-paper","tag-computer-vision","tag-cuda","tag-image-processing","tag-nvidia","tag-tesla-c870"],"views":1968,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2216","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2216"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2216\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2216"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2216"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2216"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}