Abstract
An algorithm named Minimum Cross-Entropy Algorithm (MCEA) for image reconstruction from incomplete projection data is presented here. The principle of maximum entropy has been applied to image reconstruction successfully. However, the application of principle of minimum cross-entropy to image reconstruction from incomplete projection data has not been found in literature. When the missing data or missing angle is large, the proposed algorithm yields acceptable results. Compared with the maximum entropy algorithm MENT, we conclude that (1) MCEA is superior to MENT for such cases where the number of projections involved are small; (2) Convergence performance of MCEA is better than MENT; (3) MCEA and MENT both are stable against noise; and (4) Under appropriate a priori distribution, MCEA yields satisfactory results after a couple of iterations. The speed and quality of reconstruction as well as the overhead of storage for MCEA are superior to that of MENT.
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