A new unsupervised hierarchical segmentation algorithm for textured images

An unsupervised hierarchical segmentation method is described, and its application to tissue classification in magnetic resonance (MR) images of the human brain is demonstrated. The images are modeled as a mosaic of homogeneous subimages where each subimage is modeled as a first-order Gauss-Markov random field (GMRF) with unknown parameters. The segmentation goal is to group the pixels into regions which, under a suitable hypothesis, are homogeneous GMRFs. The image is represented by a quadtree, and its segmentation is achieved by splitting and merging the image, followed by a step-wise maximum likelihood agglomerative clustering procedure. The difficulty of evaluating the likelihood for irregularly shaped regions is overcome using a highly accurate approximation for the determinant of the covariance matrix based on eigenanalysis