Title :
A new unsupervised hierarchical segmentation algorithm for textured images
Author :
Wu, Zhenyu ; Leahy, Richard
Author_Institution :
Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA
Abstract :
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
Keywords :
Markov processes; biomedical NMR; brain; patient diagnosis; picture processing; random processes; first-order Gauss-Markov random field; homogeneous subimages; human brain; textured images; tissue classification; unsupervised hierarchical segmentation algorithm; Covariance matrix; Gaussian processes; Humans; Image processing; Image segmentation; Magnetic resonance; Merging; Pixel; Radio access networks; Signal processing;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.116048