Region Detection in Medical Images

The objective of this project is to develop a technique that is reliable, adaptive, and versatile to solve the problem of region detection for a relatively wide class of medical images. Learning is essential in approaching this objective. The learning takes places in two stages: learning for automatic selection of threshold values and learning for automatic selection of the region of interest from candidate regions in the attention map. The result from the second stage is evaluated based on a learned cost measure and the outcome is fed back to the first stage when necessary. This feedback enhances the reliability of the entire system. Experiments have been conducted to approximately locate the endocardium boundaries of the left and right ventricles from gradient-echo MR images. Cardiac CT images have also been used for testing.

References

J. Weng, A. Singh, and M. Y. Chiu, “Learning-Based Ventricle Detection from Cardiac MR and CT Images,”IEEE Trans. on Medical Imaging,vol. 16, no. 4, pp. 378-391, Aug. 1997.
J. Weng, A. Singh, and M. Y. Chiu, ``Learning-based ventricle detection from cardiac MR and CT images,'' in Proc. IEEE Workshop on Biomedical Image Analysis, Seattle, Washington, pp. 23-32, June 24-25, 1994. Download PostScript .
J. Weng, A. Singh, and M. Y. Chiu, ``Fully automatic ventricle detection from cardiac MR images using machine learning,'' in Proc. SPIE Medical Imaging 1994, Newport Beach, CA, pp. 40-51, Feb. 1994.
 Back To Weng's Home Page: http://web.cps.msu.edu/~weng/