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Proceedings Paper

Multiscale based adaptive contrast enhancement
Author(s): Muhammad Abir; Fahima Islam; Daniel Wachs; Hyoung Lee
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Paper Abstract

A contrast enhancement algorithm is developed for enhancing the contrast of x-ray images. The algorithm is based on Laplacian pyramid image processing technique. The image is decomposed into three frequency sub-bands- low, medium, and high. Each sub-band contains different frequency information of the image. The detail structure of the image lies on the high frequency sub-band and the overall structure lies on the low frequency sub-band. Apparently it is difficult to extract detail structure from the high frequency sub-bands. Enhancement of the detail structures is necessary in order to find out the calcifications on the mammograms, cracks on any object such as fuel plate, etc. In our proposed method contrast enhancement is achieved from high and medium frequency sub-band images by decomposing the image based on multi-scale Laplacian pyramid and enhancing contrast by suitable image processing. Standard Deviation-based Modified Adaptive contrast enhancement (SDMACE) technique is applied to enhance the low-contrast information on the sub-bands without overshooting noise. An alpha-trimmed mean filter is used in SDMACE for sharpness enhancement. After modifying all sub-band images, the final image is derived from reconstruction of the sub-band images from lower resolution level to upper resolution level including the residual image. To demonstrate the effectiveness of the algorithm an x-ray of a fuel plate and two mammograms are analyzed. Subjective evaluation is performed to evaluate the effectiveness of the algorithm. The proposed algorithm is compared with the well-known contrast limited adaptive histogram equalization (CLAHE) algorithm. Experimental results prove that the proposed algorithm offers improved contrast of the x-ray images.

Paper Details

Date Published: 14 February 2013
PDF: 9 pages
Proc. SPIE 8657, Computational Imaging XI, 86570X (14 February 2013); doi: 10.1117/12.2005567
Show Author Affiliations
Muhammad Abir, Missouri Univ. of Science and Technology (United States)
Fahima Islam, Missouri Univ. of Science and Technology (United States)
Daniel Wachs, Idaho National Lab. (United States)
Hyoung Lee, Missouri Univ. of Science and Technology (United States)

Published in SPIE Proceedings Vol. 8657:
Computational Imaging XI
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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