Quantitative core-based shape comparison

Kevin O. Lepard; Richard A. Robb

doi:10.1117/12.274176

25 April 1997 Quantitative core-based shape comparison

Kevin O. Lepard, Richard A. Robb

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274176
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

Comparison of shapes is at best a difficult problem. Although many methods of measuring shapes are available, such as circularity, Fourier descriptors, and invariant moments, these methods generally suffer from one or more of the following drawbacks, (1) requiring previous segmentation of the shape, (2) inability to relate the metric intuitively to the shape, and (3) inability to describe local features of object shape. We describe two new metrics based on cores: the average chamfer distance and the average fractional difference. These metrics do not require prior segmentation of objects, can be used to describe local features of object shape, and are intuitively related to degree of shape similarity or dissimilarity. Furthermore, we demonstrate that these metrics are well-behaved, producing output that varies in a predictable fashion, increasing in value as shapes become increasingly different and decreasing in value as shapes become increasing similar.

Citation Download Citation

Kevin O. Lepard and Richard A. Robb "Quantitative core-based shape comparison", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274176

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KEYWORDS

Image segmentation

Biomedical optics

Computer vision technology

Machine vision

Visualization

Distance measurement

Image classification

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