Multiscale shape analysis for computed radiographic images

Ben K. Jang

doi:10.1117/12.237960

16 April 1996 Multiscale shape analysis for computed radiographic images

Ben K. Jang

Proceedings Volume 2710, Medical Imaging 1996: Image Processing; (1996) https://doi.org/10.1117/12.237960
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

Abstract

The construction of a multiscale (scale space) representation requires the smoothing of the given image to generate a set of corresponding images at other coarser scales, and the extraction of features at these scales. Various methods of smoothing, combined with various feature extractors, will result in drastically different scale space representations. The particular application and desired criteria determine the choice of smoothing and feature extractor. In this paper, we first put foreword a framework for the scale space representation. Then we focus on the Gaussian and morphological scale space for planar shape analysis of computed radiographic (CR) images. The discussion of the various scale space methods is organized into three categories -- boundary approach, region approach, and hybrid approach. Properties, limitations, performance, and applications of these scale space methods are discussed. Extensive experiments on CR images of various exam types were conducted and the results are evaluated.

Citation Download Citation

Ben K. Jang "Multiscale shape analysis for computed radiographic images", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237960

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