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Proceedings Paper

Automatic histogram-based segmentation of white matter hyperintensities using 3D FLAIR images
Author(s): Rita Simões; Cornelis Slump; Christoph Moenninghoff; Isabel Wanke; Martha Dlugaj; Christian Weimar
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Paper Abstract

White matter hyperintensities are known to play a role in the cognitive decline experienced by patients suffering from neurological diseases. Therefore, accurately detecting and monitoring these lesions is of importance. Automatic methods for segmenting white matter lesions typically use multimodal MRI data. Furthermore, many methods use a training set to perform a classification task or to determine necessary parameters. In this work, we describe and evaluate an unsupervised segmentation method that is based solely on the histogram of FLAIR images. It approximates the histogram by a mixture of three Gaussians in order to find an appropriate threshold for white matter hyperintensities. We use a context-sensitive Expectation-Maximization method to determine the Gaussian mixture parameters. The segmentation is subsequently corrected for false positives using the knowledge of the location of typical FLAIR artifacts. A preliminary validation with the ground truth on 6 patients revealed a Similarity Index of 0.73 ± 0.10, indicating that the method is comparable to others in the literature which require multimodal MRI and/or a preliminary training step.

Paper Details

Date Published: 23 February 2012
PDF: 10 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83153K (23 February 2012); doi: 10.1117/12.911327
Show Author Affiliations
Rita Simões, Univ. Twente (Netherlands)
Cornelis Slump, Univ. Twente (Netherlands)
Christoph Moenninghoff, Univ. Essen Hufelandstrasse (Germany)
Isabel Wanke, Univ. Essen Hufelandstrasse (Germany)
Martha Dlugaj, Univ. Essen Hufelandstrasse (Germany)
Christian Weimar, Univ. Essen Hufelandstrasse (Germany)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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