This paper suggests a new approach for improving the accuracy of retinal OCT frames. This is referred to as the QIROCT (Quality Enhancement of Retinal Optical Coherence Tomography) procedure. Retinal optical coherence tomography (OCT) image is a layered structure. A mixture model, combination of multiple distributions, is used to represent retinal OCT image. A Gaussian-Mixture-Model (GMM), mixture of Gaussians, is proposed to represent the retinal OCT image as retina is a layered structure. Expectation maximization (EM) is an algorithm that fits the Gaussian mixture model (GMM). Gaussian components are obtained using the Expectation Maximization (EM) algorithm to match the Gaussian Mixture Model (GMM) to the retinal OCT results. Adaptive Gamma Correction with Weighting Distribution (AGCWD) is used to improve Gaussian components of the retinal OCT image. To understand the superiority of QIROCT method for OCT image processing, 30 healthy retinal OCT images are tested. The QIROCT approach is opposed to the contrast limited adaptive histogram equalization (CLAHE) method, and the difference between the two methods is visually and numerically illustrated. And segmentation is done for retinal OCT image using the QIROCT method and results are shown.