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Proceedings Paper

Global Local Edge Coincidence Segmentation For Medical Images
Author(s): J. J. Hwang; C. C. Lee; E. L. Hall
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Paper Abstract

Object location in computed tomography images is a preliminary step required for automated measurements which may be useful in many diagnostic procedures. Most object location, image processing techniques are either globally based such as histogram segmentation or locally based such as edge detection. The method described in this paper uses both local and global information for object location. The technique has been applied to the location of suspected tumors in CT lung and brain images. Sorting and merging steps are required for eliminating noise regions but all suspected tumor regions have been located. Measurements such as boundary roughness or density statistics may also be made on the objects and used to identify suspicious regions for further study by the radiologists. Algorithms for chain-encoding the object boundaries and locating the vertices on the boundaries is also presented and compared. These methods are useful for shape analysis of the regions. The significance of this technique is that it demonstrates important additional capability which could be added to the software libraries of most CT systems.

Paper Details

Date Published: 26 December 1979
PDF: 13 pages
Proc. SPIE 0206, Recent and Future Developments in Medical Imaging II, (26 December 1979); doi: 10.1117/12.958208
Show Author Affiliations
J. J. Hwang, The University of Tennessee (United States)
C. C. Lee, The University of Tennessee (United States)
E. L. Hall, The University of Tennessee (United States)

Published in SPIE Proceedings Vol. 0206:
Recent and Future Developments in Medical Imaging II
David G. Brown; Stephen W. Smith, Editor(s)

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