title: Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines.
abstract: 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.
authors.affiliation: CEREMADE, UMR 7534 CNRS Université Paris Dauphine, France; CSIRO Preventative Health National Research Flagship ICTC, The Australian e-Health Research Centre - BioMedIA, Royal Brisbane and Women's Hospital, Herston, Qld, Australia.
authors.forename: Jean-Baptiste
authors.initials: JB
authors.lastname: Fiot
authors.forename: Laurent D
authors.initials: LD
authors.lastname: Cohen
authors.forename: Parnesh
authors.initials: P
authors.lastname: Raniga
authors.forename: Jurgen
authors.initials: J
authors.lastname: Fripp
mesh_terms: D001921:Brain / Q000033:anatomy & histology / Q000473:pathology; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D059906:Neuroimaging / Q000379:methods*; D015203:Reproducibility of Results; D060388:Support Vector Machine
keywords: brain lesion; classification; image processing; segmentation; support vector machines