A new unsupervised hierarchical segmentation algorithm for textured images
Zhenyu Wu, Richard M. Leahy · International Conference on Acoustics, Speech, and Signal Processing · 2002
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.>