Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines

Abstract

SUMMARY: Support vector machines (SVM) are machine learning techniques 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, proton-density and fluid attenuated inversion recovery(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±0.12, 0.72±0.06 and 0.82±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±0.13, 0.71±0.08 and 0.81±0.07). Furthermore, there was a negligible difference between using 5×5×5 and 3×3×3 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. © 2013 John Wiley & Sons, Ltd.