Atlas guided identification of brain structures by combining 3d segmentation and SVM classification

Authors:
Ayelet Akselrod-Ballin;Meirav Galun;Moshe John Gomori;Ronen Basri;Achi Brandt
Affiliations:
Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel;Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel;Dept. of Radiology, Hadassah University Hospital, Jerusalem, Israel;Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel;Dept. of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

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Abstract

This study presents a novel automatic approach for the identification of anatomical brain structures in magnetic resonance images (MRI). The method combines a fast multiscale multi-channel three dimensional (3D) segmentation algorithm providing a rich feature vocabulary together with a support vector machine (SVM) based classifier. The segmentation produces a full hierarchy of segments, expressed by an irregular pyramid with only linear time complexity. The pyramid provides a rich, adaptive representation of the image, enabling detection of various anatomical structures at different scales. A key aspect of the approach is the thorough set of multiscale measures employed throughout the segmentation process which are also provided at its end for clinical analysis. These features include in particular the prior probability knowledge of anatomic structures due to the use of an MRI probabilistic atlas. An SVM classifier is trained based on this set of features to identify the brain structures. We validated the approach using a gold standard real brain MRI data set. Comparison of the results with existing algorithms displays the promise of our approach.