MGDM

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A Multiple Object Geometric Deformable Model for Image Segmentation

John A. Bogovic, Jerry L. Prince, and Pierre-Louis Bazin

Abstract

Deformable models are widely used for image segmentation, most commonly to find single objects within an image. Although several methods have been proposed to segment multiple objects using deformable models, substantial limitations in their utility remain. This paper presents a multiple object segmentation method using a novel and efficient object representation for both two and three dimensions. The new framework guarantees object relationships and topology, prevents overlaps and gaps, enables boundary-specific speeds, and has a computationally efficient evolution scheme that is largely independent of the number of objects. Maintaining object relationships and straightforward use of object-specific and boundary-specific smoothing and advection forces enables the segmentation of objects with multiple compartments, a critical capability in the parcellation of organs in medical imaging. Comparing the new framework with previous approaches shows its superior performance and scalability.

Source code and demonstrations

Source code and MATLAB demonstrations can be found on the MGDM NITRC page.

• Compiled MGDM code and MATLAB scripts can be downloaded from NITRC. This release also contains several demonstrations of MGDM.
• Instructions for checking out and compiling the MGDM source code.
• Users experienced with deformable models can take full advantage of MGDM by specifying boundary-specific speeds.

Publications

About MGDM

• J.A. Bogovic, J.L. Prince, and P.-L. Bazin, "A Multiple Object Geometric Deformable Model for Image Segmentation", Computer Vision and Image Understanding, 117(2):145-157, 2013. (doi) (PubMed)
• X. Fan, P.-L. Bazin, J. Bogovic, Y. Bai, and J.L. Prince, "A multiple geometric deformable model framework for homeomorphic 3D medical image segmentation", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, Alaska, June 24-26, 2008. (doi)
• X. Fan, P.-L. Bazin, and J.L. Prince, "A Multi-Compartment Segmentation Framework With Homeomorphic Level Sets.", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), Anchorage, Alaska, June 24-26, 2008. (PubMed)

Using MGDM

• A. Carass, A. Lang, M. Hauser, P.A. Calabresi, H.S. Ying, and J.L. Prince, "Multiple-object geometric deformable model for segmentation of macular OCT", Biomedical Optics Express, 5(4):1062-1074, 2014. (doi)
• J.A. Bogovic, P.L. Bazin, S.H. Ying, and J.L. Prince, "Automated Segmentation of the Cerebellar Lobules using Boundary Specific Classification and Evolution", 23^rd Conference on Information Processing in Medical Imaging (IPMI 2013), Asilomar California, USA, June 29 - July 3, 2013. (doi)
• J. Stough, C. Ye, S.H. Ying, and J.L. Prince, "Thalamic Parcellation from Multi-Modal Data using Random Forest Learning", Tenth IEEE International Symposium on Biomedical Imaging (ISBI 2013), San Francisco, CA, April 7 - 11, 2013.
• C. Ye, J.A. Bogovic, S.H. Ying, and J.L. Prince, "Segmentation of the Complete Superior Cerebellar Peduncles using a Multi-object Geometric Deformable Model", Tenth IEEE International Symposium on Biomedical Imaging (ISBI 2013), San Francisco, CA, April 7 - 11, 2013.
• Z. Yang, J.A. Bogovic, A. Carass, M. Ye, P.C. Searson, and J.L. Prince, "Automatic Cell Segmentation in Fluorescence Images of Confluent Cell Monolayers Using Multi-object Geometric Deformable Model (MGDM)", Proceedings of SPIE Medical Imaging (SPIE-MI 2013), Orlando, FL, February 9-14, 2013. (doi)
• C. Ye, J.A. Bogovic, S.H. Ying, and Jerry L. Prince, "Parcellation of the thalamus using diffusion tensor images and a multi-object geometric deformable model", Proceedings of SPIE Medical Imaging (SPIE-MI 2013), Orlando, FL, February 9-14, 2013. (doi)