PROOF COPY 013201JEI Comparison of deconvolution techniques using a distribution mixture parameter estimation: Application in single photon emission computed tomography imagery
Max Mignotte, Jean Meunier, Ottawa Ontario K, Christian Janicki · 2002
Thanks to its ability to yield functionally rather than anatomically-based information, the single photon emission com- puted tomography (SPECT) imagery technique has become a great help in the diagnostic of cerebrovascular diseases which are the third most common cause of death in the USA and Europe. Never- theless, SPECT images are very blurred and consequently their in- terpretation is difficult. In order to improve the spatial resolution of these images and then to facilitate their interpretation by the clini- cian, we propose to implement and to compare the effectiveness of different existing ''blind'' or ''supervised'' deconvolution methods. To this end, we present an accurate distribution mixture parameter es- timation procedure which takes into account the diversity of the laws in the distribution mixture of a SPECT image. In our application, parameters of this distribution mixture are efficientlyexploited in or- der to prevent overfittingof the noisy data for the iterative deconvo- lution techniques without regularization term, or to determine the exact support of the object to be restored when this one is needed. Recent blind deconvolution techniques such as the NAS-RIF algo- rithm, (D. Kundur and D. Hatzinakos, ''Blind image restoration via recursive filtering using deterministic constraints,'' in Proc. Interna- tional Conf. On Acoustics, Speech, and Signal Processing, Vol. 4, pp. 547-549 (1996).) combined with this estimation procedure, can be efficientlyapplied in SPECT imagery and yield promising results.