基于Shearlet变换的深度学习医学图像融合新方法

A. Mergin, Godwin Premi Maria Sebastin
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引用次数: 1

摘要

多模态医学图像融合(MMIF)方法广泛应用于各种临床环境。对于专家来说,MMIF可以提供包含解剖和生理信息的图像,有助于制定诊断程序。先前提出了与MMIF相关的不同模型。但是,需要增强先前方法的功能。在该模型中,提出了一种基于最优阈值和深度学习方法的独特融合模型。一种增强的君主蝶优化算法(EMBO)利用shearlet变换中的融合规则确定最优阈值。核聚变过程的效率主要取决于核聚变规则,对核聚变规则的优化可以提高核聚变的效率。然后利用深度学习方法的提取元素融合高、低频子带。融合技术使用卷积神经网络(CNN)进行。研究采用MRI和CT图像。结果表明,该模型具有较低的误差值和较高的相关度,具有较好的融合性能。
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Shearlet Transform-Based Novel Method for Multimodality Medical Image Fusion Using Deep Learning
Multi-modality medical image fusion (MMIF) methods were widely used in a variety of clinical settings. For specialists, MMIF could provide an image containing anatomical and physiological information that can help develop diagnostic procedures. Different models linked to MMIF were proposed previously. However, there would be a need to enhance the functionality of prior methodologies. In this proposed model, a unique fusion model depending upon optimal thresholding and deep learning approaches are presented. An enhanced monarch butterfly optimization (EMBO) determines an optimal threshold with fusion rules as in shearlet transform. The efficiency of the fusion process mainly depends on the fusion rule and the optimization of the fusion rule can improve the efficiency of the fusion. The extraction element of the deep learning approach was then utilized to fuse high- and low-frequency sub-bands. The fusion technique was carried out using a convolutional neural network (CNN). The studies were carried out for MRI and CT images. The fusion results were attained and the proposed model was proved to offer effective performance with reduced values of error and improved values of correlation.
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