Format

Send to

Choose Destination
Acad Radiol. 2008 Mar;15(3):300-13. doi: 10.1016/j.acra.2007.10.012.

Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.

Author information

1
Department of Radiology, 3600 Market Street, Suite 380, University of Pennsylvania, Philadelphia, PA 19104, USA. Zhiqiang.Lao@uphs.upenn.edu

Abstract

RATIONALE AND OBJECTIVES:

Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important.

MATERIALS AND METHODS:

In this article, we present a computer-assisted WML segmentation method, based on local features extracted from multiparametric magnetic resonance imaging (MRI) sequences (ie, T1-weighted, T2-weighted, proton density-weighted, and fluid attenuation inversion recovery MRI scans). A support vector machine classifier is first trained on expert-defined WMLs, and is then used to classify new scans.

RESULTS:

Postprocessing analysis further reduces false positives by using anatomic knowledge and measures of distance from the training set.

CONCLUSIONS:

Cross-validation on a population of 35 patients from three different imaging sites with WMLs of varying sizes, shapes, and locations tests the robustness and accuracy of the proposed segmentation method, compared with the manual segmentation results from two experienced neuroradiologists.

PMID:
18280928
PMCID:
PMC2528894
DOI:
10.1016/j.acra.2007.10.012
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center