Manuscript received November 24, 2022; revised February 2, 2023; accepted March 1, 2023

Abstract—Medical images are an important source of information for both diagnosing and treating diseases. In many cases, the images produced by a Positron Emission Tomography (PET) scan are used to assess the effectiveness of a particular treatment. This paper presents a method for whole-body PET image denoising using a spatially-guided non-local means filter. The proposed method starts with clustering the images into regions. To estimate the noise, a Bayesian with automatic settings of the parameters was used. Then, only patches that belong to regions were collected and processed. The performance was compared to two methods; Gaussian and conventional Non-Local Means (NLM). The Jaszczak phantom and PET/ Computed Tomography (CT) for whole-body were involved in the benchmarking. The obtained results showed that in the Jaszczak phantom, the Signal-to-Noise Ratio (SNR) was significantly improved. Additionally, the proposed method improved the contrast and SNR compared to conventional NLM and Gaussian. Finally, the proposed method, in clinical whole-body PET, can be considered as another way of the post-reconstruction filter.

Keywords—image denoising, image enhancing, medical images, Non-Local Means (NLM) filter, Positron Emission Tomography (PET)/ Computed Tomography (CT) images

Cite: Raghad Hazim Hamid, Nagham Tharwat Saeed, and Hasan Maher Ahmed *, "A Method for Enhancing PET Scan Images Using Nonlocal Mean Filter," Journal of Image and Graphics, Vol. 11, No. 3, pp. 282-287, September 2023.

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