ArticleProceedings of SPIE--the International Society for Optical Engineering2013
Automatic Segmentation of Right Ventricle on Ultrasound Images Using Sparse Matrix Transform and Level Set.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Fully automated segmentation of the right ventricle in patients with repaired Tetralogy of Fallot using U-Net.Proceedings of SPIE--the International Society for Optical Engineering · 2020Article
- Correlated Regression Feature Learning for Automated Right Ventricle Segmentation.IEEE journal of translational engineering in health and medicine · 2018Article
- Quantitative Wavelength Analysis and Image Classification for Intraoperative Cancer Diagnosis with Hyperspectral Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2015Article
- Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Spectral-Spatial Classification Using Tensor Modeling for Cancer Detection with Hyperspectral Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Mapping Cardiac Fiber Orientations from High-Resolution DTI to High-Frequency 3D Ultrasound.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Hyperspectral Imaging for Cancer Surgical Margin Delineation: Registration of Hyperspectral and Histological Images.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging.Journal of biomedical optics · 2014Article
- Automatic segmentation of right ventricular ultrasound images using sparse matrix transform and a level set.Physics in medicine and biology · 2013Article
- 3DProceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
4 authors.
Funding
Abstract
An automatic framework is proposed to segment right ventricle on ultrasound images. This method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform (SMT), a training model, and a localized region based level set. First, the sparse matrix transform extracts main motion regions of myocardium as eigenimages by analyzing statistical information of these images. Second, a training model of right ventricle is registered to the extracted eigenimages in order to automatically detect the main location of the right ventricle and the corresponding transform relationship between the training model and the SMT-extracted results in the series. Third, the training model is then adjusted as an adapted initialization for the segmentation of each image in the series. Finally, based on the adapted initializations, a localized region based level set algorithm is applied to segment both epicardial and endocardial boundaries of the right ventricle from the whole series. Experimental results from real subject data validated the performance of the proposed framework in segmenting right ventricle from echocardiography. The mean Dice scores for both epicardial and endocardial boundaries are 89.1%±2.3% and 83.6±7.3%, respectively. The automatic segmentation method based on sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.
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