Tianli Yu's Research

During my summer internship in Kodak Research Lab, we are developing robust image segmentation methods to allow automatic analysis of X-ray images. Our algorithm can "learn" from a set of training examples what shape to look for in the new image. The resulting "ShRAC" algorithm, which stands for Shape Regularized Active Contour, has an excellent robustness to noise and distracting structures in medical images, and is able to segment objects with large (nonlinear) shape variations.

Figure 1: Starting with the same elliptic shape, a segmentation algorithm should converge to the correct shape without model selection (e.g. to adapt to whether it is the left or the right lung field). Here we show the result of the proposed ShRAC algorithm: (a) Initial contour on the right lung, (b) Final contour on the right lung, (c) Initial contour on the left lung (note that it is the same shape as in (a)), and (d) Final contour on the left lung.


Papers


Tianli Yu, Jiebo Luo, and Narendra Ahuja,
Search Strategies for Shape Regularized Active Contour, Computer Vision and Image Understanding. 2008. [Download from Science Direct]

Tianli Yu, Jiebo Luo and Narendra Ahuja
Shape Regularized Active Contour using Iterative Global Search and Local Optimization, CVPR 2005, June 20-26 2005, San Diego, CA, USA. [Download from IEEE Xplore]

Tianli Yu, Jiebo Luo, Amit Singhal, and Narendra Ahuja
Shape regularized active contour based on dynamic programming for anatomical structure segmentation, SPIE Medical Imaging 2005, February 12-17 2005, San Diego, CA, USA