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Medical & Biological Engineering & Computing

, Volume 57, Issue 12, pp 2567–2598 | Cite as

An efficient wavelet and curvelet-based PET image denoising technique

  • Abhishek BalEmail author
  • Minakshi Banerjee
  • Punit Sharma
  • Mausumi Maitra
Original Article
  • 75 Downloads

Abstract

Positron emission tomography (PET) image denoising is a challenging task due to the presence of noise and low spatial resolution compared with other imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT). PET image noise can hamper further processing and analysis, such as segmentation and disease screening. The wavelet transform–based techniques have often been proposed for PET image denoising to handle isotropic (smooth details) features. The curvelet transform–based PET image denoising techniques have the ability to handle multi-scale and multi-directional properties such as edges and curves (anisotropic features) as compared with wavelet transform–based denoising techniques. The wavelet denoising method is not optimal for anisotropic features, whereas the curvelet denoising method sometimes has difficulty in handling isotropic features. In order to handle the weaknesses of individual wavelet and curvelet-based methods, the present research proposes an efficient PET image denoising technique based on the combination of wavelet and curvelet transforms, along with a new adaptive threshold selection to threshold the wavelet coefficients in each subband (except last level low pass (LL) residual). The proposed threshold utilizes the advantages of adaptive threshold taken from BayesShrink along with the neighborhood window concept. The present method was tested on both simulated phantom and clinical PET datasets. Experimental results show that our method has achieved better results than the existing methods such as VisuShrink, BayesShrink, NeighShrink, ModineighShrink, curvelet, and an existing wavelet curvelet-based method with respect to different noise measurement metrics, such as mean squared error (MSE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and image quality index (IQI). Furthermore, notable performance is achieved in the case of medical applications such as gray matter segmentation and precise tumor region identification.

Graphical Abstract

Block diagram of the proposed method. (a) Steps of the proposed denoising method and (b) image information generated by each step of (a).

Keywords

Image denoising PET Wavelet Curvelet Thresholding Neighborhood coefficients 

Notes

Acknowledgments

Sincere gratitude to Dr. Punit Sharma, MD at Apollo Gleneagles Hospital, Kolkata, India, for providing the clinical PET brain datasets and verified the results throughout this project. The authors would like to thank Dr. Haseeb Hassan, MD, DM at Rabindranath Tagore International Institute of Cardiac Sciences, Kolkata, India, and Dr. Arindam Chatterjee, MD, at Variable Energy Cyclotron Centre (VECC), Kolkata, India for their helpful comments. The authors would like to thank the referees for providing their very valuable comments on the original version of the manuscript.

Funding information

This research work was supported by the Board of Research in Nuclear Sciences (BRNS), DAE, Government of India, under the Reference No. 34/14/13/2016-BRNS/34044.

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Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.RCC Institute of Information TechnologyKolkataIndia
  2. 2.Apollo Gleneagles HospitalKolkataIndia
  3. 3.Government College of Engineering and Ceramic TechnologyKolkataIndia

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