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Proceedings Paper

Robust binary segmentation of radiographic images by using multiscale relevance function
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Paper Abstract

Detection and binarization of local objects of interest (defects and abnormalities) in radiographic images is considered with application to industrial (non-destructive testing) and medical diagnostic imaging. The known standard approaches such as the histogram-based binarization or the method of dynamic thresholding yield poor segmentation results on the images containing small low-contrast objects and noisy background. The proposed method for object detection using binary segmentation has the following advantageous features. A model-based approach is applied which exploits the object multi-scale morphological representation in order to perform a time-effective image analysis. The intensity function is modeled by a polynomial regression representation with the so- called conformable two-region model. The estimation of the model parameters is made by using a robust non-linear estimation procedure. The concept of a multi-scale relevance function has been introduced for rapid location of local objects invariantly to the object shape, size, and orientation. The relevance function is a function that has the local maximum at the location center of an object of interest or its relevant part such as the corner edge. The developed segmentation method has been comparatively tested on radiographic images in non-destructive testing of weld joins and medical images from chest radiography.

Paper Details

Date Published: 3 March 2000
PDF: 12 pages
Proc. SPIE 3961, Nonlinear Image Processing XI, (3 March 2000); doi: 10.1117/12.379397
Show Author Affiliations
Roman M. Palenichka, Institute of Physics and Mechanics (Canada)
Uwe Zscherpel, Federal Institute for Material Research and Testing (Germany)


Published in SPIE Proceedings Vol. 3961:
Nonlinear Image Processing XI
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

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