Sparse active shape models: influence of the interpolation kernel on segmentation accuracy and speed

Federico M. Sukno; Constantine Butakoff; Bart H. Bijnens; Alejandro F. Frangi

doi:10.1117/12.844506

12 March 2010 Sparse active shape models: influence of the interpolation kernel on segmentation accuracy and speed

Federico M. Sukno, Constantine Butakoff, Bart H. Bijnens, Alejandro F. Frangi

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76234X (2010) https://doi.org/10.1117/12.844506
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

We analyze the segmentation of sparse data using the 3D variant of Active Shape Models by van Assen et al. (SPASM). This algorithm is designed to segment volumetric data represented by multiple planes with arbitrary orientations and with large undersampled regions. With the help of statistical shape constraints, the complicated interpolation of the sliced data is replaced by a mesh-based interpolation. To overcome large void areas without image information the mesh nodes are updated using a Gaussian kernel that propagates the available information to the void areas. Our analysis shows that the accuracy is mostly constant for a wide range of kernel scales, but the convergence speed is not. Experiments on simulated 3D echocardiography datasets indicate that an appropriate selection of the kernel can even double the convergence speed of the algorithm. Additionally, the optimal value for the kernel scale seems to be mainly related to the spatial frequency of the model encoding the statistical shape priors rather than to the sparsity of the sliced data. This suggests the possibility to precalculate the propagation coefficients which would reduce the computational load up to 40% depending on the spatial configuration of the input data.

Citation Download Citation

Federico M. Sukno, Constantine Butakoff, Bart H. Bijnens, and Alejandro F. Frangi "Sparse active shape models: influence of the interpolation kernel on segmentation accuracy and speed", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76234X (12 March 2010); https://doi.org/10.1117/12.844506

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KEYWORDS

Data modeling

3D modeling

Image segmentation

3D image processing

Spatial frequencies

Statistical modeling

Computer simulations

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