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An efficient method for PET image denoising by combining multi-scale transform and non-local means

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Abstract

The diagnosis of dementia, particularly in the early stages is very much helpful with Positron emission tomography (PET) image processing. The most important challenges in PET image processing are noise removal and region of interests (ROIs) segmentation. Although denoising and segmentation are performed independently, but the performance of the denoising process significantly affects the performance of the segmentation process. Due to the low signals to noise ratio and low contrast, PET image denoising is a challenging task. Individual wavelet, curvelet and non-local means (NLM) based methods are not well suited to handle both isotropic (smooth details) and anisotropic (edges and curves) features due to its restricted abilities. To address these issues, the present work proposes an efficient denoising framework for reducing the noise level of brain PET images based on the combination of multi-scale transform (wavelet and curvelet) and tree clustering non-local means (TNLM). The main objective of the proposed method is to extract the isotropic features from a noisy smooth PET image using tree clustering based non-local means (TNLM). Then curvelet-based denoising is applied to the residual image to extract the anisotropic features such as edges and curves. Finally, the extracted anisotropic features are inserted back into the isotropic features to obtain an estimated denoised image. Simulated phantom and clinical PET datasets have been used in this proposed work for testing and measuring the performance in the medical applications, such as gray matter segmentation and precise tumor region identification without any interaction with other structural images like MRI or CT. The results in the experimental section show that the proposed denoising method has obtained better performance than existing wavelet, curvelet, wavelet-curvelet, non-local means (NLM) and deep learning methods based on the preservation of the edges. Qualitatively, a notable gain is achieved in the proposed denoised PET images in terms of contrast enhancement than other existing denoising methods.

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Acknowledgment

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. Sincere gratitude to Dr. Punit Sharma, MD at Apollo Gleneagles Hospital, Kolkata, India for providing the clinical PET brain datasets and valuable comments throughout this work. 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.

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Authors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India

    Abhishek Bal & Rituparna Chaki

  2. RCC Institute of Information Technology, Kolkata, India

    Minakshi Banerjee

  3. Apollo Gleneagles Hospital, Kolkata, India

    Punit Sharma

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  1. Abhishek Bal
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Bal, A., Banerjee, M., Chaki, R. et al. An efficient method for PET image denoising by combining multi-scale transform and non-local means. Multimed Tools Appl 79, 29087–29120 (2020). https://doi.org/10.1007/s11042-020-08936-0

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