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Algorithms to automatically quantify the geometric similarity of anatomical surfaces

Author(s): Boyer, Doug M; Lipman, Yaron; St Clair, Elizabeth; Puente, Jesus; Patel, Biren A; et al

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dc.contributor.authorBoyer, Doug M-
dc.contributor.authorLipman, Yaron-
dc.contributor.authorSt Clair, Elizabeth-
dc.contributor.authorPuente, Jesus-
dc.contributor.authorPatel, Biren A-
dc.contributor.authorFunkhouser, Thomas-
dc.contributor.authorJernvall, Jukka-
dc.contributor.authorDaubechies, Ingrid-
dc.identifier.citationBoyer, Doug M., Yaron Lipman, Elizabeth St Clair, Jesus Puente, Biren A. Patel, Thomas Funkhouser, Jukka Jernvall, and Ingrid Daubechies. "Algorithms to automatically quantify the geometric similarity of anatomical surfaces." Proceedings of the National Academy of Sciences of the United States of America 108, no. 45 (2011): pp. 18221-18226. doi: 10.1073/pnas.1112822108en_US
dc.description.abstractWe describe approaches for distances between pairs of two-dimensional surfaces (embedded in three-dimensional space) that use local structures and global information contained in interstructure geometric relationships. We present algorithms to automatically determine these distances as well as geometric correspondences. This approach is motivated by the aspiration of students of natural science to understand the continuity of form that unites the diversity of life. At present, scientists using physical traits to study evolutionary relationships among living and extinct animals analyze data extracted from carefully defined anatomical correspondence points (landmarks). Identifying and recording these landmarks is time consuming and can be done accurately only by trained morphologists. This necessity renders these studies inaccessible to nonmorphologists and causes phenomics to lag behind genomics in elucidating evolutionary patterns. Unlike other algorithms presented for morphological correspondences, our approach does not require any preliminary marking of special features or landmarks by the user. It also differs from other seminal work in computational geometry in that our algorithms are polynomial in nature and thus faster, making pairwise comparisons feasible for significantly larger numbers of digitized surfaces. We illustrate our approach using three datasets representing teeth and different bones of primates and humans, and show that it leads to highly accurate results.en_US
dc.format.extent18221 - 18226en_US
dc.relation.ispartofProceedings of the National Academy of Sciences of the United States of Americaen_US
dc.rightsAuthor's manuscripten_US
dc.titleAlgorithms to automatically quantify the geometric similarity of anatomical surfacesen_US
dc.typeJournal Articleen_US

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