Statistical Surface Recovery: A Study on Ear Canals

Rasmus Ramsbøl Jensen, Oline Vinter Olesen, Rasmus Reinhold Paulsen, Mike van der Poel, Rasmus Larsen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    We present a method for surface recovery in partial surface scans based on a statistical model. The framework is based on multivariate point prediction, where the distribution of the points are learned from an annotated data set. The training set consist of surfaces with dense correspondence that are Procrustes aligned. The average shape and point covariances can be estimated from this set. It is shown how missing data in a new given shape can be predicted using the learned statistics. The method is evaluated on a data set of 29 scans of ear canal impressions. By using a leave-one-out approach we reconstruct every scan and compute the point-wise prediction error. The evaluation is done for every point on the surface and for varying hole sizes. Compared to state-of-the art surface reconstruction algorithm, the presented methods gives very good prediction results.
    Original languageEnglish
    Title of host publicationMesh Processing in Medical Image Analysis : MeshMed 2012 Proceedings
    PublisherSpringer
    Publication date2012
    Pages49-58
    ISBN (Print)978-3-642-33462-7
    ISBN (Electronic)978-3-642-33463-4
    DOIs
    Publication statusPublished - 2012
    Event15th International Conference on Medical Image Computing and Computer Assisted Intervention: Workshop on Mesh Processing in Medical Image Analysis (MeshMed) - Acropolis Convention and Exhibition Center, Nice, France
    Duration: 1 Oct 2012 → …
    http://www2.imm.dtu.dk/projects/MeshMed/index.html

    Workshop

    Workshop15th International Conference on Medical Image Computing and Computer Assisted Intervention
    LocationAcropolis Convention and Exhibition Center
    CountryFrance
    CityNice
    Period01/10/2012 → …
    Internet address
    SeriesLecture Notes in Computer Science
    Volume7599
    ISSN0302-9743

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