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Proceedings Paper

Computer-assisted diagnostic (CAD) methods for x-ray imaging and teleradiology
Author(s): Wei Qian; Lihua Li; Laurence P. Clarke; Fei Mao; Maria Kallergi; Robert A. Clark M.D.; Jerry A. Thomas
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Paper Abstract

The development and evaluation of a new class of algorithms for computer assisted diagnostic (CAD) methods for segmentation and detection of masses in digitized mammograms is reported. Both non-adaptive and adaptive methods are reported that employ two key novel CAD modules, specifically tailored for digital mammography, namely: (a) a multiorientation directional wavelet transform for removal of directional features and for the direct detection of speculations for spiculated lesions, and (b) a multiresolution wavelet transform for image enhancement to improve the segmentation of suspicious areas. The aim of the work is to provide a brief overview of both the non-adaptive and adaptive methods and comparison of their performance using computer ROC curves. An image data base containing regions of interest (ROI), enclosing all mass types and normal tissues, was used for the relative comparison of the performance, where electronic ground truth was established. The result confirm the importance of using adaptive CAD methods that should potentially allow a more generalized and robust application for larger image data bases, images generated from different sensors, or direct X-ray detection, as required for clinical trials and teleradiology applications.

Paper Details

Date Published: 1 March 1998
PDF: 11 pages
Proc. SPIE 3240, 26th AIPR Workshop: Exploiting New Image Sources and Sensors, (1 March 1998); doi: 10.1117/12.300054
Show Author Affiliations
Wei Qian, Univ. of South Florida (United States)
Lihua Li, Univ. of South Florida (United States)
Laurence P. Clarke, Univ. of South Florida (United States)
Fei Mao, Univ. of South Florida (United States)
Maria Kallergi, Univ. of South Florida (United States)
Robert A. Clark M.D., Univ. of South Florida (United States)
Jerry A. Thomas, Uniformed Services Univ. of the Health Sciences (United States)

Published in SPIE Proceedings Vol. 3240:
26th AIPR Workshop: Exploiting New Image Sources and Sensors
J. Michael Selander, Editor(s)

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