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Edge detection in noisy data using finite mixture distribution analysis

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3 Author(s)
M. Thune ; Norwegian Comput. Center, Oslo, Norway ; B. Olstad ; N. Thune

An algorithm which identifies discontinuities in noisy data is presented. The signal is modelled as step edges with additive normally distributed noise present. Using finite mixture analysis a variable number of distributions are identified together with the location of the respective edges separating them. The problem is solved using a dynamic programming approach which ensures globally optimal edge positions according to the signal model of a finite mixture of normal distributions. The computational complexity is of order MN2 where M is the number of discontinuities in the mixture and N is the number of data points in the signal. The algorithm is tested on a range of signals and yields as accurate edge positions as a corresponding square error method. Among applications for this algorithm is edge detection in medical images and examples from ultrasound imaging are included

Published in:

Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference  (Volume:1 )

Date of Conference:

13-16 Nov 1994