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Proceedings Paper

Enhanced lung cancer detection in temporal subtraction chest radiography using directional edge filtering techniques
Author(s): Hui Zhao; Shih-Chung Benedict Lo; Matthew T. Freedman M.D.; Yue Joseph Wang
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Paper Abstract

We have developed a series of directional edge enhancement and edge extraction methods that can accurately segment posterior and anterior ribs in chest radiography. These methods can also separate the lower and upper edges of ribs. The edges were first enhanced by two sets of proximate parabola curve models for left and right sides of the image. We used a directional edge filtering technique to remove low signals and noises on the edge enhanced image in the multiresolution domain. Finally, we employed a rib curve projection and reasoning method to reconstruct the rib edges and remove false edges for the upper and lower bound of the rib edges independently. A two-step registration, corresponding to global and local matching, is applied for current and prior images assisted by their corresponding edge images. The subtraction images were then processed by a rule-based CAD system. The FROC results were compared to that obtained by the original image using a CAD system consisting of rule-based and convolution neural network processing. The majority of lung cancer in temporal subtraction images were lit-up. The FROC results were significantly improved using the subtraction image with the rule-based CAD.

Paper Details

Date Published: 9 May 2002
PDF: 6 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467212
Show Author Affiliations
Hui Zhao, Georgetown Univ. Medical Ctr. and The Catholic Univ. of America (United States)
Shih-Chung Benedict Lo, Georgetown Univ. Medical Ctr. (United States)
Matthew T. Freedman M.D., Georgetown Univ. Medical Ctr. (United States)
Yue Joseph Wang, The Catholic Univ. of America (United States)

Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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