Skeletonization based on the symmetric point pairs sequence and its application

XiaoJun Li; YiXin Zhang

doi:10.1117/12.473978

22 May 2003 Skeletonization based on symmetric point pairs sequence and its application

XiaoJun Li, YiXin Zhang

Proceedings Volume 5011, Machine Vision Applications in Industrial Inspection XI; (2003) https://doi.org/10.1117/12.473978
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

Abstract

Here we focus our discussion on symmetry analysis of image. A new method, Symmetric Point Pairs Sequence (SPPS), is proposed for skeletonization and applied to process complex intersections in biomedical images. Among many thinning algorithms, methods based on contour information have been explored popular recently. In many methods based on polygonal approximation of contour, Voronoi Diagram is applied to compute the Voronoi skeleton. But for each thinning algorithm, crossing region is a rather difficult problem, in which skeleton always deforms. In this paper the SPPS is described firstly. It is obtained through the Delaunay Triangulation for sampling points of the contour. In the crossing region whose structure is represented by the triangulation dual graph model, the SPPSs are merged and reconstructed. So the skeleton that isn’t deformed is obtained. We apply this method to process complex intersections in biomedical images. In the neuroscience field, a number of pathologies seem to be connected to morphological alterations of neural cells. In our experiment a lot of symmetric point pairs are displayed. The result shows this method preserve the precise the topological relation among the crossing regions, so our purpose to individualize all the real cells by different shape is reached.

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XiaoJun Li and YiXin Zhang "Skeletonization based on symmetric point pairs sequence and its application", Proc. SPIE 5011, Machine Vision Applications in Industrial Inspection XI, (22 May 2003); https://doi.org/10.1117/12.473978

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KEYWORDS

Biomedical optics

Image processing

Image analysis

Image segmentation

Neuroscience

Pathology

Lithium

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