Differential Mean Deviation Factor based Contrast Enhancement Technique Using DWT-SVD for Non-Contrast CT Scan Images (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
In proper clinical analysis and diagnosis of a patient, accurate visual representation of CT scan images plays a vital role. Due to technical constraints and poor imaging device, sometimes low contrast images can be obtained. To interpret and analyzed the minute details present in the image, the image needs to be enhanced appropriately. This proposed novel low contrast enhancement technique rely on the singular value decomposition (SVD) with discrete wavelet transform (DWT) for improving the minute visual information present in a image as well as retaining the mean brightness. By scaling the values of the singular matrix properly these existing SVD based techniques enhances the contrast of image but the contrast in case of the original CT scan image is too low and hinder proper enhancement result in poor analysis. In this suggested algorithm, the proposed differential mean-deviation factor properly equalized the singular matrix. Introduction of this parameter adjusted the singular value matrix in such a way that proper contrast enhancement is attained. Results of the simulation also reveal that the suggested approach also balanced the brightness of the picture more reliably and increases the contrast comparatively with negligible visual artifacts. It outperforms traditional methodologies of equalization of images such as GHE (Global histogram equalization) and LHE (local histogram equalization). Our proposed algorithm is evaluated and compared using different CT scan images and different algorithms based on SVD and DWT but the inclusion of our proposed factor mean deviation provides a niche over the existing ones in terms of subjective and objective analysis.