Denoising is an important issue for medical image processing. In this paper, a fast CBCT denoising method was proposed: CBCT images were transformed into wavelet domain with dyadic wavelet transform. According to the inter-scale relationship of wavelet coefficient magnitude sum in cone of influence (COI), wavelet coefficients were classified into two categories, then different types of coefficient were denoised by different wiener filtering based on direction window at all levels, and a new noise variation estimating method more suitable for CBCT images was proposed. Experimental results of a test image and a clinical CBCT image show that this algorithm is superior to the conventional method for wavelet shrinkage denoising. This algorithm can suppress noise in CBCT images effectively and keep up the important structure details for diagnosis, thus providing a new approach for real-time denoising clinical CBCT images.
Algorithms ; Artifacts ; Artificial Intelligence ; Cone-Beam Computed Tomography ; methods ; Image Interpretation, Computer-Assisted ; methods ; Radiographic Image Enhancement ; methods ; Signal Processing, Computer-Assisted