Medical imaging is perturbed with inherent noise such as speckle noise in ultrasound, Poisson noise in X-ray and Rician noise in MRI imaging. This paper focuses on X-ray image denoising problem. X-ray image quality could be improved by increasing dose value; however, this may result in cell death or similar kinds of issues. Therefore, image processing techniques are developed to minimise noise instead of increasing dose value for patient safety. In this paper, usage of modified Harris corner point detector to predict noisy pixels and responsive median filtering in spatial domain is proposed. Experimentation proved that the proposed work performs better than simple median filter and moving average (MA) filter. The results are very close to non-local means Poisson noise filter which is one of the current state-of-the-art methods. Benefits of the proposed work are simple noise prediction mechanism, good visual quality and less execution time.

X-rays with photon having wavelength below 0.2-0.1 nm are generally used to produce X-ray images due to their high penetration ability. But photon counting statistics follows Poisson noise distribution which degrades quality of medical data (X-ray)represented by photon images. This Poisson noise can be reduced by increasing the dose of X-ray for production of X-ray image but it will harm patient body. In this paper, modified Harris operator along with wavelet domain thresholding is proposed for X-ray image denoising. Harris operator finds the pixels in image having more intensity variation compare to neighboring pixels, such pixels can be called as noisy pixels. We compare our denoised results with other denoising techniques and found significant improvement in result. IndexTerms-X-ray image, Poisson noise, waveletThresholding, Harris operator.

Medical imaging suffers from image noise. To remove this noise spatial domain and transform domain techniques are used. But spatial domain techniques have limitation of edge blurring w.r.t transform based techniques .Therefore in this paper we have proposed a transform based denoising technique. We have used Dual tree DWT and Rotated version of Dual Tree DWT jointly to improve our denoising results. Our main focus is to get more directional information from this transform which will improve denoising results. We have compared our results with existing methods and results are very encouraging. PSNR is used as image quality measure.

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