Image segmentation is an old and ever-growing field in computer vision. Several image segmentation algorithms with diverse approach methodologies have been proposed over the years. As a result, several evaluation criteria have also been proposed. Many of these segmentation and evaluation methods are based on the famous Gini index. Several have tried using entropy values as well. Methods based on these have been tuned and modified for betterment over the years. This paper does a thorough literature survey on the growth and usage of Gini index and entropy for segmentation and primarily evaluation. Realizing the potential as well as the limitations, the paper proposes an evaluation criteria based on Gini index and entropy. The proposed algorithm uses the concept of maximum intra-region homogeneity and inter-region heterogeneity in segments to evaluate a segmentation technique. Evaluation is done on segments as seen by the segmentation technique in the original input image.