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Fast curvelet transform through genetic algorithm for multimodal medical image fusion

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Abstract

Currently, medical imaging modalities produce different types of medical images to help doctors to diagnose illnesses or injuries. Each modality of images has its specific intensity. Many researchers in medical imaging have attempted to combine redundancy and related information from multiple types of medical images to produce fused medical images that can provide additional concentration and image diagnosis inspired by the information for the medical examination. We propose a new method and method of fusion for multimodal medical images based on the curvelet transform and the genetic algorithm (GA). The application of GA in our method can solve the suspicions and diffuse existing in the input image and can further optimize the characteristics of image fusion. The proposed method has been tested in many sets of medical images and is also compared to recent medical image fusion techniques. The results of our quantitative evaluation and visual analysis indicate that our proposed method produces the best advantage of medical fusion images over other methods, by maintaining perfect data information and color compliance at the base image.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61632009, 61472451 in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, and in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01.

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  1. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, 510006, China

    Muhammad Arif & Guojun Wang

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  1. Muhammad Arif
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  2. Guojun Wang
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Correspondence to Guojun Wang.

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Arif, M., Wang, G. Fast curvelet transform through genetic algorithm for multimodal medical image fusion. Soft Comput 24, 1815–1836 (2020). https://doi.org/10.1007/s00500-019-04011-5

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