A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method

Poay Hoon Lim; Ulas Bagci; Omer Aras; Yan Wang; Li Bai

doi:10.1109/ISBI.2012.6235907

A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method

Poay Hoon Lim^*, Ulas Bagci, Omer Aras, Yan Wang, Li Bai

^*Corresponding author for this work

Radiology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

Segmentation of spinal vertebrae is extremely important in the study of spinal related disease or disorders. However, limited work has been done on precise segmentation of spinal vertebrae. The complexity of vertebrae shapes, with gaps in the cortical bone, internal boundaries, as well as the noisy, incomplete or missing information from the images have undoubtedly increased the challenge for image analysis. In this paper, we introduce a novel level set segmentation framework that integrates shape prior and the Willmore flow to drive the level set evolution. While the shape energy draws the level set function towards a range of possible prior shapes, the edge-mounted Willmore energy captures the localized geometry information and smooths the surface during the level set evolution. Experimental results on segmentation of spinal vertebrae from CT images demonstrate the powerful combination of prior knowledge and geometrical flow.

Original language	English (US)
Title of host publication	2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2012 - Proceedings
Pages	1703-1706
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2012.6235907
State	Published - 2012
Event	2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain Duration: May 2 2012 → May 5 2012

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Other

Other	2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Country/Territory	Spain
City	Barcelona
Period	5/2/12 → 5/5/12

Keywords

Vertebrae segmentation
Willmore flow
computed tomography
kernel density estimation
level set method

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2012.6235907

Cite this

Lim, P. H., Bagci, U., Aras, O., Wang, Y., & Bai, L. (2012). A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method. In 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings (pp. 1703-1706). Article 6235907 (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2012.6235907

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title = "A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method",

abstract = "Segmentation of spinal vertebrae is extremely important in the study of spinal related disease or disorders. However, limited work has been done on precise segmentation of spinal vertebrae. The complexity of vertebrae shapes, with gaps in the cortical bone, internal boundaries, as well as the noisy, incomplete or missing information from the images have undoubtedly increased the challenge for image analysis. In this paper, we introduce a novel level set segmentation framework that integrates shape prior and the Willmore flow to drive the level set evolution. While the shape energy draws the level set function towards a range of possible prior shapes, the edge-mounted Willmore energy captures the localized geometry information and smooths the surface during the level set evolution. Experimental results on segmentation of spinal vertebrae from CT images demonstrate the powerful combination of prior knowledge and geometrical flow.",

keywords = "Vertebrae segmentation, Willmore flow, computed tomography, kernel density estimation, level set method",

author = "Lim, {Poay Hoon} and Ulas Bagci and Omer Aras and Yan Wang and Li Bai",

year = "2012",

doi = "10.1109/ISBI.2012.6235907",

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series = "Proceedings - International Symposium on Biomedical Imaging",

pages = "1703--1706",

booktitle = "2012 9th IEEE International Symposium on Biomedical Imaging",

note = "2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 ; Conference date: 02-05-2012 Through 05-05-2012",

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Lim, PH, Bagci, U, Aras, O, Wang, Y & Bai, L 2012, A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method. in 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings., 6235907, Proceedings - International Symposium on Biomedical Imaging, pp. 1703-1706, 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012, Barcelona, Spain, 5/2/12. https://doi.org/10.1109/ISBI.2012.6235907

A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method. / Lim, Poay Hoon; Bagci, Ulas; Aras, Omer et al.
2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings. 2012. p. 1703-1706 6235907 (Proceedings - International Symposium on Biomedical Imaging).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method

AU - Lim, Poay Hoon

AU - Bagci, Ulas

AU - Aras, Omer

AU - Wang, Yan

AU - Bai, Li

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N2 - Segmentation of spinal vertebrae is extremely important in the study of spinal related disease or disorders. However, limited work has been done on precise segmentation of spinal vertebrae. The complexity of vertebrae shapes, with gaps in the cortical bone, internal boundaries, as well as the noisy, incomplete or missing information from the images have undoubtedly increased the challenge for image analysis. In this paper, we introduce a novel level set segmentation framework that integrates shape prior and the Willmore flow to drive the level set evolution. While the shape energy draws the level set function towards a range of possible prior shapes, the edge-mounted Willmore energy captures the localized geometry information and smooths the surface during the level set evolution. Experimental results on segmentation of spinal vertebrae from CT images demonstrate the powerful combination of prior knowledge and geometrical flow.

AB - Segmentation of spinal vertebrae is extremely important in the study of spinal related disease or disorders. However, limited work has been done on precise segmentation of spinal vertebrae. The complexity of vertebrae shapes, with gaps in the cortical bone, internal boundaries, as well as the noisy, incomplete or missing information from the images have undoubtedly increased the challenge for image analysis. In this paper, we introduce a novel level set segmentation framework that integrates shape prior and the Willmore flow to drive the level set evolution. While the shape energy draws the level set function towards a range of possible prior shapes, the edge-mounted Willmore energy captures the localized geometry information and smooths the surface during the level set evolution. Experimental results on segmentation of spinal vertebrae from CT images demonstrate the powerful combination of prior knowledge and geometrical flow.

KW - Vertebrae segmentation

KW - Willmore flow

KW - computed tomography

KW - kernel density estimation

KW - level set method

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Lim PH, Bagci U, Aras O, Wang Y, Bai L. A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method. In 2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Proceedings. 2012. p. 1703-1706. 6235907. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2012.6235907

A novel spinal vertebrae segmentation framework combining geometric flow and shape prior with level set method

Abstract

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Keywords

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