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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Pages | 1122-1129 |
| Number of pages | 8 |
| Volume | 1 |
| DOIs | |
| Publication status | Published - 2006 |
| Event | 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States Duration: Jun 17 2006 → Jun 22 2006 |
Other
| Other | 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 6/17/06 → 6/22/06 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
An integrated segmentation and classification approach applied to multiple sclerosis analysis. / Akselrod-Ballin, Ayelet; Galun, Meirav; Gomori, Moshe John; Filippi, Massimo; Valsasina, Paula; Basri, Ronen; Brandt, Achi.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 1 2006. p. 1122-1129 1640876.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An integrated segmentation and classification approach applied to multiple sclerosis analysis
AU - Akselrod-Ballin, Ayelet
AU - Galun, Meirav
AU - Gomori, Moshe John
AU - Filippi, Massimo
AU - Valsasina, Paula
AU - Basri, Ronen
AU - Brandt, Achi
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33845586711&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845586711&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2006.55
DO - 10.1109/CVPR.2006.55
M3 - Conference contribution
AN - SCOPUS:33845586711
SN - 0769525970
SN - 9780769525976
VL - 1
SP - 1122
EP - 1129
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ER -