An Efficient Thresholding Neural Network Technique for High Noise Densities Environments

Medical images when infected with high noise densities lose usefulness for diagnosis and earlydetection purposes. Thresholding neural networks (TNN) with a new class of smooth nonlinearfunction have been widely used to improve the efficiency of the denoising procedure. This paperintroduces better solution for medical images in noisy environments which serves in earlydetection of breast cancer tumor. The proposed algorithm is based on two consecutive phases.Image denoising, where an adaptive learning TNN with remarkable time improvement and goodimage quality is introduced. A semi-automatic segmentation to extract suspicious regions orregions of interest (ROIs) is presented as an evaluation for the proposed technique. A set of datais then applied to show algorithm superior image quality and complexity reduction especially inhigh noisy environments.