{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T10:40:50Z","timestamp":1651056050191},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2016,4,14]],"date-time":"2016-04-14T00:00:00Z","timestamp":1460592000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Morphological attribute filters operate on images based on properties or attributes of connected\ncomponents. Until recently, attribute filtering was based on a single global threshold on a scalar property to\nremove or retain objects. A single threshold struggles in case no single property or attribute value has a suitable,\nusually multi-modal, distribution. Vector-attribute filtering allows better description of characteristic\nfeatures for 2D images. In this paper, we apply vector-attribute filtering to 3D and incorporate unsupervised\npattern recognition, where connected components are classified based on the similarity of feature vectors.\nUsing a single attribute allows multi-thresholding for attribute filters where more than two classes of structures\nof interest can be selected. In vector-attribute filters automatic clustering avoids the need for either\nsetting very many attribute thresholds, or finding suitable class prototypes in 3D and setting a dissimilarity\nthreshold. Explorative visualization reduces to visualizing and selecting relevant clusters. We show that the\nperformance of these new filters is better than those of regular attribute filters in enhancement of objects in\nmedical images.<\/jats:p>","DOI":"10.1515\/mathm-2016-0007","type":"journal-article","created":{"date-parts":[[2016,4,25]],"date-time":"2016-04-25T10:00:36Z","timestamp":1461578436000},"source":"Crossref","is-referenced-by-count":0,"title":["Cluster Based Vector Attribute Filtering"],"prefix":"10.1515","volume":"1","author":[{"given":"Fred N.","family":"Kiwanuka","sequence":"first","affiliation":[{"name":"College of Computing and Information Sciences, Makerere University, P.O.Box 7062 Kampala, Uganda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael H.F.","family":"Wilkinson","sequence":"additional","affiliation":[{"name":"Institute for Mathematics and Computing Science, University of Groningen, P.O Box 407, 9700 AK Groningen, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2016,4,14]]},"container-title":["Mathematical Morphology - Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/mathm.2016.1.issue-1\/mathm-2016-0007\/mathm-2016-0007.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mathm-2016-0007\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mathm-2016-0007\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T10:20:10Z","timestamp":1651054810000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mathm-2016-0007\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,14]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,3,30]]}},"alternative-id":["10.1515\/mathm-2016-0007"],"URL":"https:\/\/doi.org\/10.1515\/mathm-2016-0007","relation":{},"ISSN":["2353-3390"],"issn-type":[{"value":"2353-3390","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,14]]}}}