Sami Muhammad Ahsan, Tahir Muhammad Nabeel, Hassan Umer
Department of Electrical and Computer Engineering, School of Engineering, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
Global Health Institute, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
Biosensors (Basel). 2025 Jun 21;15(7):403. doi: 10.3390/bios15070403.
Fluorescence microscopy enabled by smartphone-coupled 3D instruments has shown utility in different biomedical applications ranging from diagnostics to biomanufacturing. Recently, we have designed and developed these devices and have demonstrated their utility in micro-nano particle sensing and leukocyte imaging. Here, we present a novel application for enhancing the imaging performance of smartphone fluorescence microscopes (SFM) and reducing their operational complexity. Computational noise correction is employed using 3D Averaging and 3D Gaussian filters of different kernel sizes (3 × 3 × 3, 7 × 7 × 7, 11 × 11 × 11, 15 × 15 × 15, and 21 × 21 × 21) and various standard deviations σ (for Gaussian only). Fluorescent beads of different sizes (8.3, 2, 1, 0.8 µm) were imaged using a custom-designed SFM. The application of the computational filters significantly enhanced the signal quality of particle detection in the captured fluorescent images. Amongst the Averaging filters, a kernel size of 21 × 21 × 21 produced the best results for all bead sizes, and similarly, amongst Gaussian filters, σ equal to 5 and a kernel size equal to 21 × 21 × 21 produced the best results. This visual improvement was then quantified by calculating the signal-difference-to-noise ratio (SDNR) and contrast-to-noise ratio (CNR) of filtered and unfiltered original images using a custom-developed quality assessment algorithm (AQAFI). Lastly, noise correction using Averaging and Gaussian filters with the previously identified optimal parameters was applied to images of fluorescently tagged human peripheral blood leukocytes captured using an SFM under various conditions. The ubiquitous nature and simplistic application of these filters enable their utility with a range of existing fluorescence microscope designs, thus allowing us to enhance their imaging capabilities.
由智能手机耦合的3D仪器实现的荧光显微镜已在从诊断到生物制造的不同生物医学应用中显示出实用性。最近，我们设计并开发了这些设备，并展示了它们在微纳米颗粒传感和白细胞成像中的实用性。在此，我们提出一种新颖的应用，用于增强智能手机荧光显微镜（SFM）的成像性能并降低其操作复杂性。使用不同内核大小（3×3×3、7×7×7、11×11×11、15×15×15和21×21×21）的3D平均滤波器和3D高斯滤波器以及各种标准差σ（仅适用于高斯滤波器）进行计算噪声校正。使用定制设计的SFM对不同大小（8.3、2、1、0.8 µm）的荧光珠进行成像。计算滤波器的应用显著提高了捕获的荧光图像中颗粒检测的信号质量。在平均滤波器中，21×21×21的内核大小对所有珠子大小都产生了最佳结果，同样，在高斯滤波器中，σ等于5且内核大小等于21×21×21产生了最佳结果。然后使用定制开发的质量评估算法（AQAFI）计算滤波后的原始图像和未滤波的原始图像的信号差噪比（SDNR）和对比度噪声比（CNR），对这种视觉改善进行量化。最后，使用具有先前确定的最佳参数的平均滤波器和高斯滤波器对在各种条件下使用SFM捕获的荧光标记的人类外周血白细胞图像进行噪声校正。这些滤波器的普遍性质和简单应用使其能够与一系列现有的荧光显微镜设计一起使用，从而使我们能够增强它们的成像能力。
