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Proceedings Paper

Recursive Gauss-Seidel median filter for CT lung image denoising
Author(s): Dyah Ekashanti Octorina Dewi; Ahmad Athif Mohd. Faudzi; Tati Latifah Mengko; Koichi Suzumori
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Paper Abstract

Poisson and Gaussian noises have been known to affect Computed Tomography (CT) image quality during reconstruction. Standard median (SM) Filter has been widely used to reduce the unwanted impulsive noises. However, it cannot perform satisfactorily once the noise density is high. Recursive median (RM) filter has also been proposed to optimize the denoising. On the other hand, the image quality is degraded. In this paper, we propose a hybrid recursive median (RGSM) filtering technique by using Gauss-Seidel Relaxation to enhance denoising and preserve image quality in RM filter. First, the SM filtering was performed, followed by Gauss-Seidel, and combined to generate secondary approximation solution. This scheme was iteratively done by applying the secondary approximation solution to the successive iterations. Progressive noise reduction was accomplished in every iterative stage. The last stage generated the final solution. Experiments on CT lung images show that the proposed technique has higher noise reduction improvements compared to the conventional RM filtering. The results have also confirmed better anatomical quality preservation. The proposed technique may improve lung nodules segmentation and characterization performance.

Paper Details

Date Published: 8 February 2017
PDF: 5 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102252D (8 February 2017); doi: 10.1117/12.2266968
Show Author Affiliations
Dyah Ekashanti Octorina Dewi, Univ. Teknologi Malaysia (Malaysia)
Ahmad Athif Mohd. Faudzi, Univ. Teknologi Malaysia (Malaysia)
Tati Latifah Mengko, Institut Teknologi Bandung (Indonesia)
Koichi Suzumori, Tokyo Intsitute of Technology (Japan)

Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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