Automatic MRI compression and segmentation using a stochastic model
Jamshid Dehmeshki, Orlean I. B. Cole, R. E. Marston, Mohammad F. Daemi, Ron Coxon · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
Visualization of large multidimensional magnetic resonance images (MRI) can be augmented by reducing the noise and redundancies in the data. We present details of an automatic data compression and region segmentation technique applied to medical MRI data sampled over a wide range of inversion recovery times (TI). The example images were brain slices, each one sampled with 15 different TI values, varying from 10ms to 10s. Visually, details emerged as TI increased, but some features faded at higher values. A principal component analysis reduced the data by over two thirds without noticeable loss of detail. Conventional image clustering and segmentation techniques fail to produce satisfactory results on MR images. Among the stochastic methods, independent Gaussian random field (IGRF) models were found to be suitable models when region classes have differing grey level means. We developed an automatic image segmentation technique, based on the stochastic nature of the images, that operated in two stages. First, IGRF model parameters were estimated using a modified fuzzy clustering method. Second, image segmentation was formulated as a statistical inference problem. Using a maximum likelihood function, we estimated the class status of each pixel from the IGRF model parameters. The paper elaborates on this approach and presents practical results.