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Proceedings Paper

Multiobject segmentation using coupled shape space models
Author(s): Tobias Schwarz; Tobias Heimann; Dirk Lossnitzer; Carsten Mohrhardt; Henning Steen; Urte Rietdorf; Ivo Wolf; Hans-Peter Meinzer
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Paper Abstract

Due to noise and artifacts often encountered in medical images, segmenting objects in these is one of the most challenging tasks in medical image analysis. Model-based approaches like statistical shape models (SSMs) incorporate prior knowledge that supports object detection in case of in-complete evidence from image data. In this paper, we present a method to increase information of the object's shape in problematic image areas by incorporating mutual shape information from other entities in the image. This is done by using a common shape space of multiple objects as additional restriction. Two different approaches to implement mutual shape information are presented. Evaluation was performed on nine cardiac images by simultaneous segmentation of the epi- and endocardium of the left heart ventricle using the proposed methods. The results show that the segmentation quality is improved with both methods. For the better one, the average surface distance error is approx. 40% lower.

Paper Details

Date Published: 13 March 2010
PDF: 8 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76233V (13 March 2010); doi: 10.1117/12.844223
Show Author Affiliations
Tobias Schwarz, German Cancer Research Ctr. (Germany)
Tobias Heimann, INRIA Research Ctr. (France)
Dirk Lossnitzer, Univ. Clinics Heidelberg (Germany)
Carsten Mohrhardt, Univ. Clinics Heidelberg (Germany)
Henning Steen, Univ. Clinics Heidelberg (Germany)
Urte Rietdorf, German Cancer Research Ctr. (Germany)
Ivo Wolf, Univ. of Mannheim (Germany)
Hans-Peter Meinzer, German Cancer Research Ctr. (Germany)


Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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