A Hybrid Method of Medical Image De-nosing Using Subtraction Transform and Radial Biases Neural Network - Mudgal, Ankur; Singh, Rajdeep (International Journal of Computer Sciences and Engineering, 2025)
Image processing plays an important role in medical science for the analysis of heart attack and brain stroke. During the capturing of medical image some noise is induced and makes medical image blurred and unclear. So image de-nosing process is required to make the image noise free .In this paper we propose an image de-nosing method using subtraction transform and RBF neural network. The subtraction transform used basically in the field of voice noise reduction. the RBF neural network model is very efficient due to single layer network. The process of CT and MRI gets the high component value of noise in environment. For the reduction of these noise we have used spectral subtraction de-noising method. The spectral substation method is well recognized method for voice noise reduction. In spectral subtraction method the local noise component value are not considered. In this paper, we discuss image de-nosing methodology based on RBF neural network model comprised of radial biases neural network (RBF). The image features are extracted from the image using SSD function. RBF acts as a clustering mechanism that projects N-dimensional features from the SSD function into an M-dimensional feature space. The resulting vectors are fed into an RBF that categorizes them onto one of the relearned noise classes.