The innovation of applications such as Medical Imaging, Microscopic Imaging, Computer Vision, Remote Sensing, and Robotics rely exclusively on the successful fusion of images acquired from different modalities or instruments. Images of the same modality taken simultaneously from different places under different conditions are fused together to generate a single image with information content from both the images. Such a type of fusion is called multi-view image fusion that has been discussed in this study. The transverse abdominal scan taken with the right side of the maternal abdomen appears on the left side of the monitor, whereas, the maternal left side appears on the right of the monitor. Image fusion aims at combining multi-view ultrasound images of the placenta into a single image to obtain relevant information from the source images using the fusion technique. The resultant fusion image combines the richness of information from all the source images. The findings of this study point toward the improved performance of Wavelet image fusion in comparison to the image averaging and PCA methods. The information entropy is comparatively better when the images are fused using Wavelet. The mutual information obtained is relatively good signifying a richness of information. The Wavelet decomposed images when subject to image fusion increase the quality of information in the images. Thus, the essential features that characterize the placenta can be extracted. The boundary information and structural details are preserved without the introduction of other inconsistencies to the image. The Spatial Frequency, Fusion Mutual Information, Entropy, Peak Signal to Noise Ratio, and Root Mean Square Error prove to be good evaluators of fusion methods suitable for ultrasound of placenta images. Among these measures, Entropy seems to be an enhanced indicator of the performance of fusion methods. © Research India Publications.