This work introduces a novel and efficient algorithm for reconstructing the 3D shapes of tumors from a set of 2D bioluminescence images which are taken by the same camera but after continually rotating the animal by a small angle. The method is efficient and robust enough to be used for analyzing the repeated imaging of a same animal transplanted with gene marked cells. There are several steps in our algorithm. First, the silhouettes (or boundaries) of the animal and its interior hot spots (corresponding to tumors) are segmented in the set of bioluminescence images. Second, the images are registered according to the projection of the animal rotating axis. Third, the images are mapped onto 3D projection planes and from the viewpoint of each plane, the visual hulls of the animal and its interior tumors are reconstructed. Then, the intersection of visual hulls from all viewpoints approximates the shape of the animal and its interior tumors. In order to visualize in 3D the structure of the tumor, we also co-register the BLI-reconstructed crude structure with detailed anatomical structure extracted from high-resolution micro-CT on a single platform. The experimental results show promising performance of our reconstruction and co-registration method.

Publications

• Junzhou Huang, Xiaolei Huang, Dimitris Metaxas, Debarata Banerjee, ”3D Tumor Shape Reconstruction from 2D Bioluminescence Images and Registration with CT Images”, 1st Workshop on Microscopic Image Analysis with Applications in Biology, MIAAB’06, 2006. (Oral)
• Junzhou Huang, Xiaolei Huang, Dimitris Metaxas, Debarata Banerjee, ”3D Tumor Shape Reconstruction from 2D Bioluminescence Images” , IEEE Int’l Symposium on Biomedical Imaging: From Nano to Macro, ISBI’06, pp. 606-609, 2006. (Oral)

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