Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines
Résumé
Support Vector Machines (SVM) are a machine learning technique that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines advanced pre-processing, tissue-based feature selection and SVM classification to obtain efficient and accurate WMH segmentation. Features from 125 patients, generated from up to four MR modalities (T1-w, T2-w, PD and FLAIR), differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all four modalities gave the best overall classification (average Dice scores of $0.54 \pm 0.12$, $0.72 \pm 0.06$ and $0.82 \pm 0.06$ respectively for small, moderate and severe lesion loads); this was not significantly different ($p=0.50$) from using just T1-w and FLAIR sequences (Dice scores of $0.52 \pm 0.13$, $0.71 \pm 0.08$ and $0.81 \pm 0.07$). Furthermore, there was a negligible difference between using $5 \times 5 \times 5$ and $3 \times 3 \times 3$ features ($p=0.93$). Finally, we show that careful consideration of features and pre-processing techniques not only saves storage space and computation time but also leads to more efficient classification which outperforms the one based on all features with post-processing.
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_FIOT_IJNM2013_Efficicient_brain_lesion_segmentation_using_multi-modality_tissue-based_feature_selection_and_SVM.pdf (581.61 Ko)
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