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An automatic segmentation approach using a variant of Hough forests with implicit shape and appearance priors was proposed and evaluated on 30 3D cardiac ultrasound images from 15 patients.
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A novel Hough forest-based machine learning approach enables automatic segmentation of the left ventricle in 3D cardiac ultrasound images.
We propose a learning based approach to perform automatic segmentation of the left ventricle in 3D cardiac ultrasound images. The segmentation contour is estimated through the use of a variant of Hough forests whose object localization capabilities are coupled with a patch-wise, appearance driven, contour estimation strategy. The performance of the proposed method is evaluated on a dataset of 30 images acquired from 15 patients using different equipment and settings.
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Milletarì et al. (2014) studied this question. Hough-forests with implicit shape and appearance priors was evaluated on Segmentation contour estimation performance. An automatic segmentation approach using a variant of Hough forests with implicit shape and appearance priors was proposed and evaluated on 30 3D cardiac ultrasound images from 15 patients.
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