Abstract
Various applications in positron emission tomography (PET), single photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI) require segmentation of 20 to 60 high resolution images of size 256/spl times/256 pixels in 3-9 seconds per image. This places particular constraints on the design of image segmentation algorithms. This paper proposes a quantized data representation and a quantised EM algorithm for estimating the parameters of a finite mixture density function to be used in a Bayes classifier for image segmentation. Both a Monte Carlo evaluation and an application to MRI images showed that the quantized EM algorithm can dramatically reduce the required computation time with negligible difference in mean estimation error and mean classification error.
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