Blind deconvolution of human brain SPECT images using a distribution mixture estimation

Max Mignotte; Jean Meunier

doi:10.1117/12.387647

6 June 2000 Blind deconvolution of human brain SPECT images using a distribution mixture estimation

Max Mignotte, Jean Meunier

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387647
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

Abstract

Thanks to its ability to yield functionally-based information, the SPECT imagery technique has become a great help in the diagnostic of cerebrovascular diseases. Nevertheless, due to the imaging process, SPECT images are blurred and consequently their interpretation by the clinician is often difficult. In order to improve the spatial resolution of these images and then to facilitate their interpretation, we propose herein to implement a deconvolution procedure relying on an accurate distribution mixture parameter estimation procedure. Parameters of this distribution mixture are efficiently exploited in order to prevent overfitting of the noisy data or to determine the support of the object to be deconvolved when this one is needed. In this context, we compare the deconvolution results obtained by the Lucy-Richardson method and by the recent blind deconvolution technique called the NAS-RIF algorithm on real and simulated brain SPECT images. The NAS-RIF performs the best and shows significant contrast enhancement with little mottle (noise) amplification.

Citation Download Citation

Max Mignotte and Jean Meunier "Blind deconvolution of human brain SPECT images using a distribution mixture estimation", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387647

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