Medical Imaging Fusion Techniques: A Survey Benchmark Analysis, Open Challenges and Recommendations

Abstract

Background—Recent improvements in image processing along with active collaborations of health experts have started an era of inventions in medical imaging. From the last two decades, computer vision empowers computers to analyze the data in bulk using machine learning methods in developing intelligent models. Several techniques are already available in the literature that is capable of learning intricate patterns to generate meaningful output. A set of areas, which researchers have preferably covered are related to contrast stretching, segmentation, feature extraction/fusion, and classification. In the medical domain, feature fusion is an active area, which plays a vital role in the final classification step. Objective—In this review article, our primary objective is to discuss and validate the advantages of feature fusion and present its roles in this domain. The fusion of two images or multiple features gives better results in the form of either detection or classification of infected areas. For this purpose, we discuss a set of techniques used by several researchers in the medical domain. Method—A detailed and comprehensive review of the fusion techniques are presenting. The key challenges and shortcomings of existing image and feature fusion methods are presenting along with the possible future directions. Conclusion—At the end of this review, we conclude that fusion techniques improves the image quality, as well as for salient (infected) regions detection. Moreover, the fusion of medical images increases the overall segmentation accuracy, and feature fusion shows its effects in the final stage of classification.

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