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Medical Image - Atlas Registration Using Deformable Models for Anomaly Detection

Mei Chen, Takeo Kanade, and Dean Pomerleau
Proceedings of the Image Understanding Workshop, November, 1998.


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Abstract
We introduce a system that automatically segments and classifies structures in brain MRI volumes. It segments 144 structures of a 256x256x124 voxel image in 18 minutes on an SGI computer with four 194 MHz R10K processors. The algorithm uses an atlas, a hand segmented and classified MRI of a normal brain, which is warped in 3-D using a hierarchical deformable registration algorithm until it closely matches the subject. This customized atlas contains the segmentation and classification of the subject's anatomical structures. The system has processed 198 MRIs of normal brains, and 3 MRIs and 1 CT of brains with pathologies. Quantitative evaluations yield high segmentation accuracy. Combined with domain knowledge, the registration algorithm is able of detecting asymmetries and abnormal variations in the subject's data that indicate the existence and location of pathologies.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Medical Robotics Technology Center
Associated Project(s): Knowledge-Guided Deformable Registration

Text Reference
Mei Chen, Takeo Kanade, and Dean Pomerleau, "Medical Image - Atlas Registration Using Deformable Models for Anomaly Detection," Proceedings of the Image Understanding Workshop, November, 1998.

BibTeX Reference
@inproceedings{Chen_1998_2237,
   author = "Mei Chen and Takeo Kanade and Dean Pomerleau",
   title = "Medical Image - Atlas Registration Using Deformable Models for Anomaly Detection",
   booktitle = "Proceedings of the Image Understanding Workshop",
   month = "November",
   year = "1998",
}

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