24 March 2014 Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers
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Abstract
Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer’s discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature’s and MR sequence’s contribution.
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Oskar Maier, Matthias Wilms, Janina von der Gablentz, Ulrike Krämer, Heinz Handels, "Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903504 (24 March 2014); doi: 10.1117/12.2043494; https://doi.org/10.1117/12.2043494
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