Trans pixelate substitution scheme for denoising computed tomography images towards high diagnosis accuracy.

Abstract

Medical images are obtained from different optical scanners and devices to provide in-body diagnosis and detection. Such scanned/acquired images are tampered with/ distorted by the unnecessary noise present in the pixel levels. A Trans-Pixelate Denoising Scheme (TPDS) is implemented to denoise these pictures to enhance the diagnosis's precision. This scheme is specific for CT images with high noise between pixelated and non-pixelated boundaries. Therefore, the boundary detected from an input CT image is suggested for a trans-pixel substitution using a two-layer neural network. The first layer is responsible for verifying the substitution-based diagnosis accuracy, and the second is identifying trans-pixels that improve accuracy. The outcome of the neural network is used to train the noisy inputs under either of the conditions to improve the diagnosis accuracy. The Proposed TPDS improves diagnosis accuracy, precision, and pixel detection by 7.3%, 8.14%, and 13.05% under different trans-pixel rates/boundaries. Under the same variant, this scheme reduces error and detection time by 11.15% and 9.03%, respectively.