Automatic segmentation and classification of multiple sclerosis in multichannel MRI.

Ayelet Akselrod-Ballin, Meirav Galun, John Moshe Gomori, Massimo Filippi, Paola Valsasina, Ronen Basri, Achi Brandt

Research output: Contribution to journalArticlepeer-review


We introduce a multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in automatically detecting multiple sclerosis (MS) lesions in 3-D multichannel magnetic resonance (MR) images. Our method uses segmentation to obtain a hierarchical decomposition of a multichannel, anisotropic MR scans. It then produces a rich set of features describing the segments in terms of intensity, shape, location, neighborhood relations, and anatomical context. These features are then fed into a decision forest classifier, trained with data labeled by experts, enabling the detection of lesions at all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. We provide experiments on two types of real MR images: a multichannel proton-density-, T2-, and T1-weighted dataset of 25 MS patients and a single-channel fluid attenuated inversion recovery (FLAIR) dataset of 16 MS patients. Comparing our results with lesion delineation by a human expert and with previously extensively validated results shows the promise of the approach.

Original languageEnglish
Pages (from-to)2461-2469
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number10
Publication statusPublished - Oct 2009

ASJC Scopus subject areas

  • Medicine(all)


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