Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification

Zhiqiang Lao; Dinggang Shen; Abbas Jawad; Bilge Karacali; Dengfeng Liu; Elias R. Melhem; R. Nick Bryan; Christos Davatzikos

Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification

Zhiqiang Lao, Dinggang Shen, Abbas Jawad, Bilge Karacali, Dengfeng Liu, Elias R. Melhem, R. Nick Bryan, Christos Davatzikos

School of Medicine

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

24 Scopus citations

Abstract

This paper presents a fully automatic white matter lesion (WML) segmentation method, based on local features determined by combining multiple MR acquisition protocols, including T1-weighted, T2-weighted, proton density (PD)-weighted and fluid attenuation inversion recovery (FLAIR) scans. Support vector machines (SVMs) are used to integrate features from these 4 acquisition types, thereby identifying nonlinear imaging profiles that distinguish and classify WMLs from normal brain tissue. Validation on a population of 45 diabetes patients with diverse spatial and size distribution of WMLs shows the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from two experienced neuroradiologists.

Original language	English (US)
Title of host publication	2006 3rd IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro - Proceedings
Pages	307-310
Number of pages	4
State	Published - Nov 17 2006
Event	2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Arlington, VA, United States Duration: Apr 6 2006 → Apr 9 2006

Publication series

Name	2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings
Volume	2006

Other

Other	2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Country/Territory	United States
City	Arlington, VA
Period	4/6/06 → 4/9/06

ASJC Scopus subject areas

Engineering(all)

Cite this

Lao, Z., Shen, D., Jawad, A., Karacali, B., Liu, D., Melhem, E. R., Bryan, R. N., & Davatzikos, C. (2006). Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification. In 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings (pp. 307-310). Article 1624914 (2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings; Vol. 2006).

Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification. / Lao, Zhiqiang; Shen, Dinggang; Jawad, Abbas et al.
2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. 2006. p. 307-310 1624914 (2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings; Vol. 2006).

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

Lao, Z, Shen, D, Jawad, A, Karacali, B, Liu, D, Melhem, ER, Bryan, RN & Davatzikos, C 2006, Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification. in 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings., 1624914, 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings, vol. 2006, pp. 307-310, 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, United States, 4/6/06.

Lao Z, Shen D, Jawad A, Karacali B, Liu D, Melhem ER et al. Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification. In 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. 2006. p. 307-310. 1624914. (2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings).

Lao, Zhiqiang ; Shen, Dinggang ; Jawad, Abbas et al. / Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification. 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. 2006. pp. 307-310 (2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings).

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abstract = "This paper presents a fully automatic white matter lesion (WML) segmentation method, based on local features determined by combining multiple MR acquisition protocols, including T1-weighted, T2-weighted, proton density (PD)-weighted and fluid attenuation inversion recovery (FLAIR) scans. Support vector machines (SVMs) are used to integrate features from these 4 acquisition types, thereby identifying nonlinear imaging profiles that distinguish and classify WMLs from normal brain tissue. Validation on a population of 45 diabetes patients with diverse spatial and size distribution of WMLs shows the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from two experienced neuroradiologists.",

author = "Zhiqiang Lao and Dinggang Shen and Abbas Jawad and Bilge Karacali and Dengfeng Liu and Melhem, {Elias R.} and Bryan, {R. Nick} and Christos Davatzikos",

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AB - This paper presents a fully automatic white matter lesion (WML) segmentation method, based on local features determined by combining multiple MR acquisition protocols, including T1-weighted, T2-weighted, proton density (PD)-weighted and fluid attenuation inversion recovery (FLAIR) scans. Support vector machines (SVMs) are used to integrate features from these 4 acquisition types, thereby identifying nonlinear imaging profiles that distinguish and classify WMLs from normal brain tissue. Validation on a population of 45 diabetes patients with diverse spatial and size distribution of WMLs shows the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from two experienced neuroradiologists.

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Automated segmentation of white matter lesions in 3D brain MR images, using multivariate pattern classification

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