ESTIMATING THE BIAS FIELD OF MR-IMAGES

We propose a modification of Wells et al, technique for bias field est imation and segmentation of magnetic resonance (MR) images, We show th at replacing the class other, which includes all tissue not modeled ex plicitly by Gaussians with small variance, by a uniform probability de nsity, and amending the expectation-maximization (EM) algorithm approp riately, gives significantly better results. We next consider the esti mation and filtering of high-frequency information in MR images, compr ising noise, intertissue boundaries, and within tissue microstructures , We conclude that post filtering is preferable to the prefiltering th at has been proposed previously, We observe that the performance of an y segmentation algorithm, in particular that of Wells et al, (and our refinements of it) is affected substantially by the number and selecti on of the tissue classes that are modeled explicitly, the correspondin g defining parameters and, critically, the spatial distribution of tis sues in the image, We present an initial exploration to choose automat ically the number of classes and the associated parameters that give t he best output, This requires us to define what is meant by ''best out put'' and for this we propose the application of minimum entropy, The methods developed have been implemented and are illustrated throughout on simulated and real data (brain and breast MR).