| 1. |
- Hansson, Mattias, et al.
(författare)
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Convex spatio-temporal segmentation of the endocardium in ultrasound data using distribution and shape priors
- 2011
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Ingår i: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781424441273
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Konferensbidrag (refereegranskat)abstract
- We present a convex variational active contour model with shape priors, for spatio-temporal segmentation of the endocardium in 2D B-mode ultrasound sequences, which can be solved by Continuous Cuts. A four component (signal dropout, echocardiographic artifacts, blood and tissue) Rayleigh mixture model is proposed for modeling the inside and outside of the endocardium. The parameters of the mixture model are determined by Expectation Maximization, for the sequence. Annotated data is used to provide prior data, by which prior distributions for the inside and outside of the endocardium are constructed. Segmentation is then achieved by minimizing the Hellinger distance between prior and estimated distributions, under the constraints of a statistical shape prior built from principal eigenvectors of the annotated data. Since our model is convex, we can employ a fast optimization method: the Split-Bregman algorithm. Promising segmentation results and quantitative measures are provided.
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| 2. |
- Klein, T., et al.
(författare)
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Spatial statistics based feature descriptor for RF ultrasound data
- 2011
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Ingår i: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781424441273
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Konferensbidrag (refereegranskat)abstract
- In this paper we present a feature descriptor, based on a Markov random field (MRF) texture model, for radio-frequency (RF) ultrasound data. The proposed approach combines global data statistics in terms of a maximum-likelihood-estimated (MLE) distribution with local pattern characteristics employing MRF interaction parameters. This combining approach facilitates the encoding of the underlying nature of the ultrasound envelope data and therefore represents a powerful feature descriptor. Applicability and performance is showcased on RF data from a human neck.
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| 3. |
- Moreno, Rodrigo, et al.
(författare)
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Soft classification of trabeculae in trabecular bone
- 2011
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Ingår i: Biomedical Imaging: From Nano to Macro, 2011. - : IEEE. - 9781424441273 ; , s. 1641-1644
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Konferensbidrag (refereegranskat)abstract
- Classification of trabecular bone aims at discriminating different types of trabeculae. This paper proposes a method to perform a soft classification from binary 3D images. In a first step, the local structure tensor is used to estimate a membership degree of every voxel to three different classes, plate-, rod- and junction-like trabeculae. In a second step, the global structure tensor of plate-like trabeculae is compared with the local orientation of rod-like trabeculae in order to discriminate aligned from non-aligned rods. Results show that soft classification can be used for estimating independent parameters of trabecular bone for every different class, by using the classification as a weighting function.
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| 4. |
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