Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Automatic Identification of Gray Matter Structures from MRI to Improve the Segmentation of White Matter Lesions

Institution:
1School of Computer Science and Engineering, University of New South Wales, Sydney, Australia.
2Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston MA, USA.
Publisher:
J Image Guid Surg
Publication Date:
Jan-1995
Volume Number:
1
Issue Number:
6
Pages:
326-38
Citation:
J Image Guid Surg. 1995 Jan;1(6):326-38.
PubMed ID:
9080353
Appears in Collections:
SPL, CNI, CRL
Generated Citation:
Warfield S.K., Dengler J., Zaers J., Guttmann C.R.G., Wells III W.M., Ettinger G.J., Hiller J., Kikinis R. Automatic Identification of Gray Matter Structures from MRI to Improve the Segmentation of White Matter Lesions. J Image Guid Surg. 1995 Jan;1(6):326-38. PMID: 9080353.
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The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity-based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML. We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patient's brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three-dimensional, region-growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two-class minimum-distance classifier. Analysis of double-echo spin-echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the cortex and deep gray matter structures provided anatomical context. This was found to improve the segmentation of MS lesions by allowing correct classification of the white matter region despite the overlapping tissue class distributions of gray matter and MS lesion.

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