Image quality assessment (IQA) is a process of measurement of the image quality using the evaluations of subjective value with the model of computation. The quality of the image can be calculated by using different types of method where each method works with using isolated features of image. One very renowned method is structural similarity index (SSIM) which measured the quality of image comparing structure of image and the structure stage is obtained from pixel-based stage. FSIM (Feature Similarity Index) measured image quality using low level feature and Gradient magnitude (GM) act as primary feature of image. In this work, a novel MFSIM (Moderate Feature Similarity Index) is introduced which work with full reference IQA, HVS (Human Visual System) and low-level feature of images. In MFSIM the Phase Congruency (PC) is used as primary feature where the PC is dimensionless contrast invariant. In the moderated FSIM the Gradient Magnitude (GM) of the image is considered as the feature of secondary. For application IQA, we applied into segmented image with original image using MRI images. The distortion level of the segmented image is calculated using different image quality index measurement techniques. The image can be used in numerous purposes and the quality of image is distorted for different reason. There are lots of applications where noise less of perfect image is used for getting exact result. So it is very important to find out the distortion level of image. For instance during the segmentation of MRI image for brain tumor detection, the exactness of image need to calculate so that the brain tumor can be find out accurately. So the main purpose of this research work is to introduce a new image quality index and find out the brain tumor and the segmented image quality.