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A dynamical appearance model based on multiscale sparse representation: segmentation of the left ventricle from 4D echocardiography.

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  • 1Electrical Engineering, Yale University, New Haven, CT, USA. xiaojie.huang@yale.edu

Abstract

The spatio-temporal coherence in data plays an important role in echocardiographic segmentation. While learning offline dynamical priors from databases has received considerable attention, these priors may not be suitable for post-infarct patients and children with congenital heart disease. This paper presents a dynamical appearance model (DAM) driven by individual inherent data coherence. It employs multi-scale sparse representation of local appearance, learns online multiscale appearance dictionaries as the image sequence is segmented sequentially, and integrates a spectrum of complementary multiscale appearance information including intensity, multiscale local appearance, and dynamical shape predictions. It overcomes the limitations of database-driven statistical models and applies to a broader range of subjects. Results on 26 4D canine echocardiographic images acquired from both healthy and post-infarct subjects show that our method significantly improves segmentation accuracy and robustness compared to a conventional intensity model and our previous single-scale sparse representation method.

PMID:
23286114
[PubMed - indexed for MEDLINE]
PMCID:
PMC3889160
Free PMC Article
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