Hybrid Wavelet-Deep Learning Framework for Fluorescence Microscopy Images Enhancement

IF 3 4区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC International Journal of Imaging Systems and Technology Pub Date : 2024-11-07 DOI:10.1002/ima.23212
Francesco Branciforti, Maura Maggiore, Kristen M. Meiburger, Tania Pannellini, Massimo Salvi
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Abstract

Fluorescence microscopy is a powerful tool for visualizing cellular structures, but it faces challenges such as noise, low contrast, and autofluorescence that can hinder accurate image analysis. To address these limitations, we propose a novel hybrid image enhancement method that combines wavelet-based denoising, linear contrast enhancement, and convolutional neural network-based autofluorescence correction. Our automated method employs Haar wavelet transform for noise reduction and a series of adaptive linear transformations for pixel value adjustment, effectively enhancing image quality while preserving crucial details. Furthermore, we introduce a semantic segmentation approach using CNNs to identify and correct autofluorescence in cellular aggregates, enabling targeted mitigation of unwanted background signals. We validate our method using quantitative metrics, such as signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR), demonstrating superior performance compared to both mathematical and deep learning-based techniques. Our method achieves an average SNR improvement of 8.5 dB and a PSNR increase of 4.2 dB compared with the original images, outperforming state-of-the-art methods such as BM3D and CLAHE. Extensive testing on diverse datasets, including publicly available human-derived cardiosphere and fluorescence microscopy images of bovine endothelial cells stained for mitochondria and actin filaments, showcases the flexibility and robustness of our approach across various acquisition conditions and artifacts. The proposed method significantly improves fluorescence microscopy image quality, facilitating more accurate and reliable analysis of cellular structures and processes, with potential applications in biomedical research and clinical diagnostics.

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荧光显微镜图像增强的混合小波-深度学习框架
荧光显微镜是可视化细胞结构的强大工具,但它也面临着诸如噪声、低对比度和自发荧光等挑战,这些都会阻碍准确的图像分析。为了解决这些局限性,我们提出了一种新型混合图像增强方法,该方法结合了基于小波的去噪、线性对比度增强和基于卷积神经网络的自荧光校正。我们的自动方法采用哈小波变换进行降噪,并采用一系列自适应线性变换进行像素值调整,在保留关键细节的同时有效提高了图像质量。此外,我们还引入了一种使用 CNN 的语义分割方法,用于识别和校正细胞聚集体中的自发荧光,从而有针对性地减少不必要的背景信号。我们使用信噪比(SNR)和峰值信噪比(PSNR)等定量指标验证了我们的方法,结果表明,与数学技术和基于深度学习的技术相比,我们的方法性能更优。与原始图像相比,我们的方法实现了 8.5 dB 的平均 SNR 提升和 4.2 dB 的 PSNR 提升,优于 BM3D 和 CLAHE 等最先进的方法。在不同的数据集上进行的广泛测试,包括公开的人源性心肌层以及线粒体和肌动蛋白丝染色的牛内皮细胞荧光显微镜图像,展示了我们的方法在各种采集条件和伪影下的灵活性和鲁棒性。所提出的方法大大提高了荧光显微镜图像的质量,有助于对细胞结构和过程进行更准确、更可靠的分析,有望应用于生物医学研究和临床诊断。
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来源期刊
International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术
CiteScore
6.90
自引率
6.10%
发文量
138
审稿时长
3 months
期刊介绍: The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.
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