A multi-strategy method for MRI segmentation

Abstract

An accurate method for T2-weighted MRI segmentation according to tissue transversal magnetization decay rates is presented. By means of a sequence of geometric image filters a classification of the pixels' intensity decay curves is provided. This can be done through a double strategy: First a log-convexity filter is applied in order to regularize image intensity decay by adjusting its geometrical properties to those that are expected from noiseless data, i.e., monotonous and convex behavior. In doing so, image noise is somewhat filtered and controlled. Data points are fitted by an over determined interpolation procedure. Decay rate distributions are obtained and tissue classification is performed by means of the determination of principal decay rates or decay modes using a suitable mathematical morphology operator, i.e., watershed or similar. Image segmentation is performed by linear regression analysis on a pixel by pixel basis assuming that the pixel intensity decay is composed by a linear superposition of the decay modes previously obtained from the decay rate distribution function. The main advantage of the proposed multi-strategy approach rests in the accuracy and speed of calculation with respect to other methods such as Inverse Laplace Transform algorithms. The method could be easily extended to any exponentially decaying set of images such as diffusion-weighted MRI.