An Integrated Segmentation and Classification Approach Applied to Multiple Sclerosis Analysis

Authors:
Ayelet Akselrod-Ballin;Meirav Galun;Ronen Basri;Achi Brandt;Moshe John Gomori;Massimo Filippi;Paula Valsasina
Affiliations:
Weizmann Institute of Science, Rehovot, Israel;Weizmann Institute of Science, Rehovot, Israel;Weizmann Institute of Science, Rehovot, Israel;Weizmann Institute of Science, Rehovot, Israel;Hadassah University Hospital, Jerusalem, Israel;Hospital San Raffaele, Milan, Italy;Hospital San Raffaele, Milan, Italy
Venue:
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Year:
2006

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Abstract

We present a novel multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in detecting multiple sclerosis lesions in 3D MRI data. Our method uses segmentation to obtain a hierarchical decomposition of a multi-channel, anisotropic MRI scan. It then produces a rich set of features describing the segments in terms of intensity, shape, location, and neighborhood relations. These features are then fed into a decision tree-based classifier, trained with data labeled by experts, enabling the detection of lesions in 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 showing successful detections of lesions in both simulated and real MR images.