Multi-Frame Low-Dose CT Image noise reduction using Adaptive Type-2 Fuzzy filter and Fast-ICA

Decreasing the absorbed dosage by patient in x-ray imaging along with keeping image quality is one of the long-term goals of medical imaging field. Using low-dose images, instead of normal-dose images, can decrease the absorbed dosage; however, it also decreases the image quality due to quantum noise. In this paper, combination of Fast-ICA and adaptive Type-2 Fuzzy filter is utilized for filtering a group of low-dose images. Five different phantoms are used for investigating various effect of denoising, such as retaining slice geometry, high resolution, low-contrast, uniformity and bead geometry regions. Due to few numbers of images (8 images for each phantom), using deep learning method is not practical. The main novelty is attempting to convert the shot noise distribution to salt and pepper and denoising mapped image using fast independent component analysis. Concisely, the average and standard deviation of PSNR and SSIM of the proposed algorithm on five phantoms are 36.0 +/- 2.7 dB and 0.83 +/- 0.2, respectively, which shows a significant improvement comparing to the similar benchmark methods.