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Proceedings of CAD'16, 2016, 301-305
On the Nesting of a Contour Dataset

John K. Johnstone, University of Alabama Birmingham

Abstract. Contour datasets arise naturally in biomedical imaging and GIS, from CT/MR scans that model anatomy and topographic maps that model terrain. The nesting of a contour inside another contour is an important property of a contour dataset.  Nesting is important because it defines the inside of the shape.  Contours at even nesting levels represent the object of interest, while contours at odd nesting levels represent holes.  Since nesting captures the position of the inside of a shape relative to a contour, the nesting level of a contour dictates its treatment in many algorithms.  For example, during reconstruction, contours at odd nesting levels should be connected only to other contours at odd nesting levels.  As the nesting behaviour of a contour dataset affects most downstream analysis of that contour dataset, it is important to analyze nesting early in the processing of a dataset, essentially the first analysis after reading.  The challenge is to make the nesting analysis efficient and, most importantly, robust to dirty datasets.  Contour datasets are often dirty, which complicates a nesting analysis.  Fortunately, it turns out that a good nesting analysis can actually be used to repair the dataset while it determines nesting.

Keywords. Contour reconstruction, nesting, biomedical modeling.

DOI: 10.14733/cadconfP.2016.301-305