Robust Nonparametric Segmentation of Infarct Lesion from Diffusion-Weighted MR Images

Magnetic Resonance Imaging (MRI) is increasingly used for the diagnosis and monitoring of neurological disorders. In particular Diffusion-Weighted MRI (DWI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke. Cerebral infarction lesion segmentation from DWI is accomplis...

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Bibliographic Details
Main Authors: Hevia-Montiel, N., Jimenez-Alaniz, J.R., Medina-Banuelos, V., Yanez-Suarez, O., Rosso, C., Samson, Y., Baillet, S.
Format: Conference Proceeding
Language:English
Subjects:
Brain
Brain MRI
cerebral ischemia
DWI
edge detection
Hospitals
Image edge detection
Image segmentation
Ischemic pain
Lesions
Magnetic resonance imaging
mean shift
Neuroscience
non parametric estimation
Pathology
Robustness
stroke
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Summary:Magnetic Resonance Imaging (MRI) is increasingly used for the diagnosis and monitoring of neurological disorders. In particular Diffusion-Weighted MRI (DWI) is highly sensitive in detecting early cerebral ischemic changes in acute stroke. Cerebral infarction lesion segmentation from DWI is accomplished in this work by applying nonparametric density estimation. The quality of the class boundaries is improved by including an edge confidence map, that is the confidence of truly being in the presence of a border between adjacent regions. The adjacency graph, that is constructed with the label regions, is analyzed and pruned to merge adjacent regions. The method was applied to real images, keeping all parameters constant throughout the process for each data set. The combination of region segmentation and edge detection proved to be a robust automatic technique of segmentation from DWI images of cerebral infarction regions in acute ischemic stroke. In a comparison with the reference infarct lesions segmentation, the automatic segmentation presented a significant correlation (r = 0.935), and an average Tanimoto index of 0.538.
ISSN:1094-687X
1558-4615
DOI:10.1109/IEMBS.2007.4352736