An efficient method for PET image denoising by combining multi-scale transform and non-local means
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An efficient method for PET image denoising by combining multi-scale transform and non-local means Abhishek Bal1 · Minakshi Banerjee2 · Rituparna Chaki1 · Punit Sharma3 Received: 28 May 2019 / Revised: 14 February 2020 / Accepted: 13 April 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
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.
Abhishek Bal
[email protected] 1
A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India
2
RCC Institute of Information Technology, Kolkata, India
3
Apollo Gleneagles Hospital, Kolkata, India
Multimedia Tools and Applications
Keywords PET · Denoising · Wavelet · Curvelet · Non-local means · Residual image
1 Introduction Positron emission tomography (PET) technology is used in medical studies and several research purposes involved in living the organs like the brain. Identification of the brain volume of the PET image is the basic task of analyzing the functional images. The functional d
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