Analysis and processing of decayed log CT image based on multifractal theory

Nondestructive log testing technology is a new subject that has recently seen rapid development. X-ray computed tomography (CT) scanning technology has been applied to the detection of internal defects in logs for the purpose of obtaining prior information that can be used to arrive at better log sawing decisions. Because sawyers currently cannot see the inside of a log until the log faces are revealed by sawing, there is little perceived need to obtain scanned images as detailed as those obtained by CT imaging system. Thus, the recognition of internal defects has become increasingly important. The traditional means of describing objects, based on the well-known Euclidean geometry, is not capable of describing different natural objects and phenomena. On the contrary, fractal geometry and its multifractal extension are new tools which can be used for describing, modeling, analyzing and processing different complex shapes and images. A method in log CT image edge detection based on multifractal theory was applied in this paper. The Holder exponent a (x,y) of image pixels was computed first, then its multifractal spectrum f (alpha) was estimated and different image pixels were classified, with f(alpha) = 1 corresponding to smoothing edge point and 1 <= f(alpha) < 1.5 to a singular edge point. Based on multifractal theory, the set of both singular edge points and smoothing edge points is the set of image edge points. Experimental results showed that the method of log CT image in the edge detection based on multifractal theory was a more effective and more local method than classical method of edge testing. (C) 2008 Elsevier B.V. All rights reserved.