Abstract
Nowadays, various medical X-ray imaging methods such as digital radiography, computed tomography and fluoroscopy are used as important tools in diagnostic and operative processes especially in the computer and robotic assisted surgeries. The procedures of extracting information from these images require appropriate deblurring and denoising processes on the pre- and intra-operative images in order to obtain more accurate information. This issue becomes more considerable when the X-ray images are planned to be employed in the photogrammetric processes for 3D reconstruction from multi-view X-ray images since, accurate data should be extracted from images for 3D modelling and the quality of X-ray images affects directly on the results of the algorithms. For restoration of X-ray images, it is essential to consider the nature and characteristics of these kinds of images. X-ray images exhibit severe quantum noise due to limited X-ray photons involved. The assumptions of Gaussian modelling are not appropriate for photon-limited images such as X-ray images, because of the nature of signal-dependant quantum noise. These images are generally modelled by Poisson distribution which is the most common model for low-intensity imaging. In this paper, existing methods are evaluated. For this purpose, after demonstrating the properties of medical X-ray images, the more efficient and recommended methods for restoration of X-ray images would be described and assessed. After explaining these approaches, they are implemented on samples from different kinds of X-ray images. By considering the results, it is concluded that using PURE-LET, provides more effective and efficient denoising than other examined methods in this research.
Highlights
There are different types of medical X-ray images such as digital radiography, computed radiography, computed tomography and fluoroscopy which are widely applied for operating planning, diagnosis and treatment processes
After demonstrating the properties of medical X-ray images, existing methods for restoration of X-ray images will be described and assessed. Various approaches such as methods based on Variance Stabilising transformation, Total Variation and Tikhonov regularization with different data fit functions, methods, Partial differential equations (PDEs) and complex diffusion processes, Block-matching and 3D filtering methods, and Poisson unbiased risk estimate (PURE)-linear expansion of thresholds (LET) are explained
For choosing an appropriate denoising method in medical photogrammetric applications, the performance of the denoising algorithms designed for Poisson noise beside some strong denoising methods not proposed for this kind of noise, has been evaluated
Summary
There are different types of medical X-ray images such as digital radiography, computed radiography, computed tomography and fluoroscopy which are widely applied for operating planning, diagnosis and treatment processes. Gaussian modelling assumptions are accurate when the sources of error are signal-independent They are not suitable for photon-limited images such as X-ray images, because of the nature of signal-dependant quantum noise. After demonstrating the properties of medical X-ray images, existing methods for restoration of X-ray images will be described and assessed Various approaches such as methods based on Variance Stabilising transformation, Total Variation and Tikhonov regularization with different data fit functions, methods, PDE and complex diffusion processes, Block-matching and 3D filtering methods, and PURE-LET are explained. These methods will be applied and implemented on image samples from three kinds of X-ray images which are radiography, CT, and fluoroscopic images with different levels of noise. Different metrics such as PSNR (peak signal to noise ratio) are explored and applied for the assessment
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