使用基于差分进化的人工兔子优化算法改进噪声医学图像的混合融合

IF 1.7 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Niladri Shekhar Mishra, Supriya Dhabal
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引用次数: 0

摘要

本文研究了从多模态医学图像中去除噪声以确保高效医学图像融合(MIF)的问题。所提出的 MIF 通过一种新型混合图像融合方案实现了最佳效果。该方案是通过改进人工兔子优化(ARO)算法的性能和新型级联组合滤波器实现的。经典 ARO 算法的探索机制结合了差分进化所采用的方法,因此被称为基于差分进化的人工兔子优化(DEARO)。通过对 CEC 2017 基准函数的测试,证明了新型 DEARO 算法的有效性,并注意到所提出的方法比现有优化算法提供了更优越的解决方案。为了证明所提方法的性能,对十个图像融合质量评价指标进行了比较。考虑到互信息(MI),所提出的方法在融合干净图像时平均提高了(40%)。同样,当源图像的两种模式都受到方差为0.1的高斯、盐和胡椒以及斑点噪声的污染时,MI值也有了(50%)、(36%)和(21%)的改善。对融合图像的定性评估表明,与当代方法相比,所提出的方案在多模态 MIF 方面取得了进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An improved hybrid fusion of noisy medical images using differential evolution-based artificial rabbits optimization algorithm

An improved hybrid fusion of noisy medical images using differential evolution-based artificial rabbits optimization algorithm

This article investigates the problem of removing noise from multi-modal medical images to ensure efficient Medical Image Fusion (MIF). The proposed MIF achieves optimal results with a novel hybrid image fusion scheme. This scheme is achieved with an improved performance of the Artificial Rabbits Optimization (ARO) algorithm and a novel cascaded combination of filters. The exploring mechanism of the classical ARO algorithm is enriched by incorporating the approaches adopted in Differential Evolution and thus termed Differential Evolution-based Artificial Rabbits Optimization (DEARO). The effectiveness of the novel DEARO algorithm is proven through the testing of the CEC 2017 benchmark functions and it is noticed that the proposed approach offers superior solutions than existing optimization algorithms. Ten image fusion quality evaluation metrics are compared to demonstrate the performance of the proposed approach. Considering Mutual Information (MI), the proposed method exhibits \(40\%\) average improvements in the fusion of clean images. Similarly, \(50\%\), \(36\%\), and \(21\%\) improvements are noticed in MI values when both the modalities of source images are contaminated with Gaussian, Salt & Pepper, and Speckle noises of variance 0.1. The qualitative evaluation of the fused image shows the advancement of the proposed scheme in multi-modal MIF compared to the contemporary approaches.

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来源期刊
Multidimensional Systems and Signal Processing
Multidimensional Systems and Signal Processing 工程技术-工程:电子与电气
CiteScore
5.60
自引率
8.00%
发文量
50
审稿时长
11.7 months
期刊介绍: Multidimensional Systems and Signal Processing publishes research and selective surveys papers ranging from the fundamentals to important new findings. The journal responds to and provides a solution to the widely scattered nature of publications in this area, offering unity of theme, reduced duplication of effort, and greatly enhanced communication among researchers and practitioners in the field. A partial list of topics addressed in the journal includes multidimensional control systems design and implementation; multidimensional stability and realization theory; prediction and filtering of multidimensional processes; Spatial-temporal signal processing; multidimensional filters and filter-banks; array signal processing; and applications of multidimensional systems and signal processing to areas such as healthcare and 3-D imaging techniques.
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