Multiobject segmentation using coupled shape space models
2010
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.
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