Enhancement of DICOM Image using combination of CLAHE, Gamma correction and Unsharp masking

Digital imaging and communications in medicine (DICOM) is an accepted standard for storing and sharing medical images. DICOM images often have low contrast, noise, or low quality, making interpretation difficult. This paper is to enhance DICOM images using a diverse range of advanced modern image processing techniques. The methodology used is a multi-step process such as contrast limited adaptive histogram equalization (CLAHE) for localized contrast enhancement, Gamma correction to modify dynamic ranges, and unsharp masking to sharpen edges and boost small intensity differences. The combination of these methods allow for even sharper visual contrast with minimal compromise of structure. Their performance was statistically evaluated using the augmented images’ peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics. The proposed algorithm generates a very good PSNR value of 55.9821 and an SSIM value of 0.9951. The results reveal a significant enhancement of image resolution as well as improving diagnostic applications of the enhanced images. This project builds a solid foundation on which to upgrade DICOM images that can be customized and incorporated into different medical imaging pipelines to improve precision and productivity.