Region-Based Segmentation and Wiener Pilot-Based NovelAmoeba Denoising Scheme for CT Imaging

Computed tomography (CT) is one of the most common and beneficial medical imaging schemes, but the associated high radiationdose injurious to the patient is always a concern. Therefore, postprocessing-based enhancement of a CT reconstructed imageacquired using a reduced dose is an active research area. Amoeba- (or spatially variant kernel-) basedfiltering is a strongcandidate scheme for postprocessing of the CT image, which adapts its shape according to the image contents. In the reportedresearch work, the amoebafiltering is customized for postprocessing of CT images acquired at a reduced X-ray dose. Theproposed scheme modifies both the pilot image formation and amoeba shaping mechanism of the conventional amoebaimplementation. The proposed scheme uses a Wienerfilter-based pilot image, while region-based segmentation is used foramoeba shaping instead of the conventional amoeba distance-based approach. The merits of the proposed scheme include beingmore suitable for CT images because of the similar region-based and symmetric nature of the human body anatomy, imagesmoothing without compromising on the edge details, and being adaptive in nature and more robust to noise. The performanceof the proposed amoeba scheme is compared to the traditional amoeba kernel in the image denoising application for CT imagesusingfiltered back projection (FBP) on sparse-view projections. The scheme is supported by computer simulations using fan-beam projections of clinically reconstructed and simulated head CT phantoms. The scheme is tested using multiple imagequality matrices, in the presence of additive projection noise. The scheme implementation significantly improves the imagequality visually and statistically, providing better contrast and image smoothing without compromising on edge details.Promising results indicate the efficacy of the proposed scheme.