Title: An effective image denoising using PPCA and classification of CT images using artificial neural networks
Authors: L. Mredhula; M.A. Dorairangaswamy
Addresses: Sathyabama University, Chennai, India ' St. Peter's University, Avadi, Chennai, India
Abstract: The main aim of denoising is to remove the noise while recollecting as much possible important signal features. This appears to be very simple when considered under practical situations, where the type of images and noises are all variable parameters. This paper deals with removal of combination of noises from image and classification of normal and abnormal images. At first phase, median filter is used to remove the noises present in the images. To improve the denoised output, we are using PSM and PPCA with morphological operations, filter and region props. In the second phase, to analyse the denoised output, neural network-based classification is proposed. The use of artificial intelligent techniques for classification shows a great potential in this field. Hence the performance of neural network classifier is estimated in terms of training performance and classification accuracy and is compared with the existing method to show the system is effective.
Keywords: image denoising; artificial neural networks; ANNs; computed tomography; CT images; PPCA; probabilistic PCA; principle component analysis; Gaussian noise; pixel surge model; PSM; GLCM; grey level co-occurrence matrix; median filter; medical images.
International Journal of Medical Engineering and Informatics, 2017 Vol.9 No.1, pp.30 - 46
Available online: 09 Nov 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article