Detection of locally stationary region for universal GMM and its application in denoising X-ray CT images

Motohiro Tabuchi, Nobumoto Yamane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

An adaptive Wiener filter (AWF) for denoising X-ray CT image has been proposed based on the universal Gaussian mixture distribution model (UNI-GMM). The universal model can be estimated by an assumption that the GMM is stationary. In the previous UNI-GMM-AWF method, a fixed observation block size of UNI-GMM has been adopted, assuming smaller block size makes the block more stationary, but the small block tend to suffer observation error due to image noise. Thus in the previous method, the observation region size was not small enough to satisfy the stationary assumption. Inversely the observation region size is not large enough for precise model detection and high denoising ability in stationary region. To overcome the problems, variable observation block sizes of the UNI-GMMs are adopted in this paper. Actually, in the new UNI-GMM-AWF method, two sizes of the UNI-GMMs are applied for each observation region and the most stationary UNI-GMM for each observation region is selected according to the normalized likelihood function, related to the Akaike's information criteria (AIC)[1]. Moreover, the new UNI-GMM which has a observation region with hole in its central region is applied to detect a small point shape structure like a small vessel or a bronchiole. Then the new UNI-GMM using observation region with hole is also selected for each observation block based on the AIC. Simulation results show that the proposed method performs better than median filter as a standard method in terms of the denoising and point like shadow preservation ability. Furthermore a simulation result shows that the new UNI-GMM-AWF is more flexible than the previous UNI-GMM-AWF method in terms of the applicability of fitting the stationary model.

Original languageEnglish
Title of host publicationWMSCI 2011 - The 15th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
Pages158-163
Number of pages6
Volume2
Publication statusPublished - 2011
Event15th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2011 - Orlando, FL, United States
Duration: Jul 19 2011Jul 22 2011

Other

Other15th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2011
CountryUnited States
CityOrlando, FL
Period7/19/117/22/11

Fingerprint

X rays
Adaptive filters
Median filters

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

Cite this

Tabuchi, M., & Yamane, N. (2011). Detection of locally stationary region for universal GMM and its application in denoising X-ray CT images. In WMSCI 2011 - The 15th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings (Vol. 2, pp. 158-163)

Detection of locally stationary region for universal GMM and its application in denoising X-ray CT images. / Tabuchi, Motohiro; Yamane, Nobumoto.

WMSCI 2011 - The 15th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. Vol. 2 2011. p. 158-163.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tabuchi, M & Yamane, N 2011, Detection of locally stationary region for universal GMM and its application in denoising X-ray CT images. in WMSCI 2011 - The 15th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. vol. 2, pp. 158-163, 15th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2011, Orlando, FL, United States, 7/19/11.
Tabuchi M, Yamane N. Detection of locally stationary region for universal GMM and its application in denoising X-ray CT images. In WMSCI 2011 - The 15th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. Vol. 2. 2011. p. 158-163
Tabuchi, Motohiro ; Yamane, Nobumoto. / Detection of locally stationary region for universal GMM and its application in denoising X-ray CT images. WMSCI 2011 - The 15th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings. Vol. 2 2011. pp. 158-163
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