Luo Wenbin, Wang Pei, Zhang Yiwei, Shi Gengqiang
The School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Oct 25;41(5):1078-1084. doi: 10.7507/1001-5515.202403054.
Image fusion currently plays an important role in the diagnosis of prostate cancer (PCa). Selecting and developing a good image fusion algorithm is the core task of achieving image fusion, which determines whether the fusion image obtained is of good quality and can meet the actual needs of clinical application. In recent years, it has become one of the research hotspots of medical image fusion. In order to make a comprehensive study on the methods of medical image fusion, this paper reviewed the relevant literature published at home and abroad in recent years. Image fusion technologies were classified, and image fusion algorithms were divided into traditional fusion algorithms and deep learning (DL) fusion algorithms. The principles and workflow of some algorithms were analyzed and compared, their advantages and disadvantages were summarized, and relevant medical image data sets were introduced. Finally, the future development trend of medical image fusion algorithm was prospected, and the development direction of medical image fusion technology for the diagnosis of prostate cancer and other major diseases was pointed out.
图像融合目前在前列腺癌（PCa）的诊断中发挥着重要作用。选择并开发一种良好的图像融合算法是实现图像融合的核心任务，它决定了所获得的融合图像质量是否良好以及能否满足临床应用的实际需求。近年来，它已成为医学图像融合的研究热点之一。为了对医学图像融合方法进行全面研究，本文综述了近年来国内外发表的相关文献。对图像融合技术进行了分类，将图像融合算法分为传统融合算法和深度学习（DL）融合算法。分析比较了一些算法的原理和工作流程，总结了它们的优缺点，并介绍了相关的医学图像数据集。最后，展望了医学图像融合算法的未来发展趋势，指出了用于前列腺癌等重大疾病诊断的医学图像融合技术的发展方向。
