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23 February 2012 Automatic histogram-based segmentation of white matter hyperintensities using 3D FLAIR images
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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.
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Rita Simões, Cornelis Slump, Christoph Moenninghoff, Isabel Wanke, Martha Dlugaj, and Christian Weimar "Automatic histogram-based segmentation of white matter hyperintensities using 3D FLAIR images", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83153K (23 February 2012); https://doi.org/10.1117/12.911327
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