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Proceedings Paper

Adaptive edge detection based on 3D kernel functions for biomedical image analysis
Author(s): Edisson Alban; Jussi Tohka; Ulla Ruotsalainen
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Paper Abstract

New adaptive edge detection algorithms based on volumetric neighborhood size estimation for automatic three or higher dimensional biomedical image analysis are presented in this work. The proposed methods are based on nonparametric three-dimensional kernel functions obtained using the "three-term" orthogonal-type polynomial equations for different types of orthogonal polynomial families. The obtained multidimensional kernels can be of any volumetric neighborhood size and order of approximation. The optimal sizes of volume estimates, produced by the multidimensional convolution of the kernels with the multidimensional biomedical images, are controlled by a switch type variance dependent volume size selector. The proposed methods show excellent results in approximating the true position and shape of the edges of different organs of the human body represented in multidimensional biomedical images, which can have nonuniform voxel size and anisotropic image intensity and noise distribution.

Paper Details

Date Published: 1 March 2005
PDF: 12 pages
Proc. SPIE 5672, Image Processing: Algorithms and Systems IV, (1 March 2005); doi: 10.1117/12.586565
Show Author Affiliations
Edisson Alban, Tampere Univ. of Technology (Finland)
Jussi Tohka, Tampere Univ. of Technology (Finland)
Univ. of California/Los Angeles (United States)
Ulla Ruotsalainen, Tampere Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 5672:
Image Processing: Algorithms and Systems IV
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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