Abstract
In the field of cell and molecular biology, green fluorescent protein (GFP) images provide functional information embodying the molecular distribution of biological cells while phase-contrast images maintain structural information with high resolution. Fusion of GFP and phase-contrast images is of high significance to the study of subcellular localization, protein functional analysis, and genetic expression. This paper proposes a novel algorithm to fuse these two types of biological images via generative adversarial networks (GANs) by carefully taking their own characteristics into account. The fusion problem is modelled as an adversarial game between a generator and a discriminator. The generator aims to create a fused image that well extracts the functional information from the GFP image and the structural information from the phase-contrast image at the same time. The target of the discriminator is to further improve the overall similarity between the fused image and the phase-contrast image. Experimental results demonstrate that the proposed method can outperform several representative and state-of-the-art image fusion methods in terms of both visual quality and objective evaluation.
Highlights
In the field of cell and molecular biology, fluorescent imaging and phase-contrast imaging are two representative imaging approaches
We propose a novel green fluorescent protein (GFP) and phasecontrast image fusion method based on generative adversarial networks (GANs). e fusion problem is modelled as an adversarial game between a generator and discriminator
By referring to the input images, it can be seen that our method achieves high performance in terms of the preservation of functional and structural information. e main contributions of this paper are summarized as follows: (1) We propose a deep learning- (DL-) based GFP and phase-contrast image fusion method via generative adversarial networks (GANs)
Summary
In the field of cell and molecular biology, fluorescent imaging and phase-contrast imaging are two representative imaging approaches. E GFP image contains functional information related to the molecular distribution of biological cells but has very low spatial resolution. E phase-contrast image provides structural information with high resolution. Fusion of GFP image and phase-contrast image is of great significance to the localization of subcellular structure, the functional analysis of protein, and the expression of gene [1]. In most of the existing image fusion methods, the role of each input image is equivalent in terms of the fusion system, which means that the input images generally undergo identical transforms and uniform fusion rules. For the problem of GFP and phase-contrast image fusion, considering that the input images vary significantly from each other, different roles can be assigned to them in the fusion system by carefully addressing their own characteristics, which is likely to provide a more effective way to tackle this fusion issue
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