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Atlas Guided Identification of Brain Structures by Combining 3D Segmentation and SVM Classification
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Institution: |
1Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel. 2Department of Radiology, Hadassah University Hospital, Jerusalem, Israel. |
Publisher: |
Med Image Comput Comput Assist Interv. MICCAI 2006 |
Publication Date: |
Oct-2006 |
Volume Number: |
9 |
Issue Number: |
2 |
Pages: |
209-216 |
Citation: |
Int Conf Med Image Comput Comput Assist Interv. 2006;9(Pt 2):209-16. |
PubMed ID: |
17354774 |
Appears in Collections: |
SLICER, Miscellaneous |
Sponsors: |
Grant #2002/254 Israel Institute of Technology IST-2002-506766 European Commission Project |
Generated Citation: |
Akselrod-Ballin A., Galun M., Gomori M.J., Basri R., Brandt A. Atlas Guided Identification of Brain Structures by Combining 3D Segmentation and SVM Classification. Int Conf Med Image Comput Comput Assist Interv. 2006;9(Pt 2):209-16. PMID: 17354774. |
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| Paper: | Download, View online |
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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.
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