Manuscript received August 1, 2022; revised September 16, 2022; accepted September 21, 2022.

Abstract—The presence of Poisson noise in medical X-ray images leads to degradation of the image quality. The obscured information is required for accurate diagnosis. During X-ray image acquisition process, weak light results into limited number of available photons, which leads into the Poisson noise commonly known as X-ray noise. Currently, the available X-ray noise removal methods have not yet obtained satisfying total denoising results to remove noise from the medical X-ray images. The available techniques tend to show good performance when the image model corresponds to the algorithm’s assumptions used but in general, the denoising algorithms fail to do complete denoise. X-ray image quality could be improved by increasing the X-ray dose value (beyond the maximum medically permissible dose) but the process could be lethal to patients’ health since higher X-ray energy may kill cells due to the effects of higher dose values. In this study, the hybrid model that combines the Poisson Principal Component Analysis (Poisson PCA) with the nonlocal (NL) means denoising algorithm is developed to reduce noise in images. This hybrid model for X-ray noise removal and the contrast enhancement improves the quality of X-ray images and can, thus, be used for medical diagnosis. The performance of the proposed hybrid model was observed by using the standard data and was compared with the standard Poisson algorithms.

Keywords—hybrid model, noise reduction, non-local PCA, Poisson PCA, X-ray image

Cite: Daniel Kipele and Kenedy A. Greyson, "Poisson Noise Reduction with Nonlocal-PCA Hybrid Model in Medical X-ray Images," Journal of Image and Graphics, Vol. 11, No. 2, pp. 178-184, June 2023.

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