A method for optimizing image-reconstruction algorithms is presented that is based on how well the specified task of object localization can be performed using the reconstructed images. The task performance is numerically assessed by a Montc Carlo simulation of the complete imaging process including the generation of scenes appropriate to the desired application, subsequent data taking, image recovery, and performance of the stated task based on the final image. This method is used to optimize tile constrained Algebraic Reconstruction Technique (ART), which reconstructs images from their projections under a nonnegativity constraint by means of an iterative updating procedure. The optimization is performed by finding tile the relaxation factor, which is employed ill the updating procedure, that yields the minimum rms error in estimating the position of discs in the reconstructed images. It is found that the optimum operating points for the best object localization are essentially the same as those obtained earlier when the performance of simple object detection is to be optimized.
Keywords: task performance, evaluation of image-processing algorithms, algebraic reconstruction technique (ART), nonnegativity constraint, deletectability, detectability index, Monto Carlo technique
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