Vertebral segmentation using contourlet-based salient point matching and localized multiscale shape prior

Zewail, Rami; Elsafi, Ahmed; Durdle, N.G.

doi:10.1117/12.812729

Cited by 3 publications

(3 citation statements)

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“…We model variations in the shape of anatomical structure of interest using the multi-shape model presented in [24]. Using wavelet-packets and Independent Component Analysis, the model extracts multi-scale localized modes of deformations.…”

Section: Multi-scale Shape Modelmentioning

confidence: 99%

“…We learn the shape space using the multi-scale shape model presented in [24]. The generative Contourlet enhanced …”

Section: Wij II Denotes the Application Of The Warping Function Wij mentioning

confidence: 99%

“…Ground truth data is needed for a number of tasks such as: validation of segmentation and registration algorithms, and improving the training phase in statistical models. The proposed appearance simulation framework makes use of the new proposed sparse appearance model, along with the multi-scale shape model presented in [24].…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

Zewail¹,

Hag-ElSafi²

2017

IJIVP

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Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

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Section: Multi-scale Shape Modelmentioning

confidence: 99%

“…We learn the shape space using the multi-scale shape model presented in [24]. The generative Contourlet enhanced …”

Section: Wij II Denotes the Application Of The Warping Function Wij mentioning

confidence: 99%