Preprocessing of lung images with a novel image denoising technique for enhancing the quality and performance

Abstract

Recently, digital image processing has attracted many researchers due to its significant performance in real-time applications such as bio-medical systems, security systems and automated computerized diagnosis systems. Lung cancer detection and diagnosis system is one such real-time health care application which requires automated processing, where in, the images are captured and processed through computer. Although various automated systems are already in place for this application, precise automatic lung cancer detection still remains a challenging task for researchers because of the unwanted signals get added into original signal during image capturing process which may degrade the image quality that intern resulting in degraded performance. In order to avoid this, image preprocessing has become an important stage with the key components as edge detection, image resampling, image enhancement and image denoising for enhancing the quality of input image. This paper aims to improve the quality of lung image with image denoising technique for enhancing the overall performance of the automated diagnosis system. A novel approach for image denoising by applying pixel classification using Multinomial Logistic Regression (MLR) and Gaussian Conditional Random Field (GCRF) is proposed in this paper. Proposed approach comprising of two major steps such as parameters generation by considering MLR and designing an inference network whose layer perform the computations which are tangled in GCRF formulation. Experimental analysis is done on LIDC, an open source benchmark database and the performance is compared with the state-of-the-art filtering schemes. Results show that proposed approach performs better in respect of PSNR values by factor 2.7 when compared to existing approaches.