Products: LabWindows/CVI, Motion Control, LabVIEW, Machine Vision, Academic Products . The Challenge: To implement hardware construction and software design for digital image sequence acquisition, image processing, spectral classification of biological cells, image selection, and optical density measurement, enabling qualitative (What), quantitative (How Many), and localization (Where) analysis of tissue cells. The Solution: A Microscopic Fluorescence Spectroscopic Imaging System (MFSIS) based on LabVIEW software. Utilizing NI-VISA tools, serial communication is used to achieve precise displacement control of the key spectral device—the linear variable filter (LVF). CVI dynamic link functions are used to complete the hardware driver for the image acquisition card, and the NI Vision Development Module is used to complete the analysis and processing of time-series and spectral image sequences. Digital image acquisition, image processing, and spectral image analysis in biomedicine are implemented on the LabVIEW platform. The system structure is complex, but the development cycle is significantly shortened. Composition of a Microscopic Fluorescence Spectroscopic Imaging System A microscopic fluorescence spectroscopy imaging system (MFSIS) comprises the following components: a light source, a spectroscopic system, a fluorescence microscope, an image adapter, a high-performance cooled CCD camera, an image acquisition card, image generation and processing, and image display. The system structure is shown in Figure 1. [align=center] Figure 1: Structure of a Microscopic Fluorescence Spectroscopic Imaging System[/align] Working Process: A high-power monochromatic excitation light source excites the sample under the microscope, causing it to emit specific biological fluorescence. According to Stocks' law, the fluorescence wavelength is greater than the excitation wavelength, and a spectroscopic element can be used to separate the two spectrally. In the system, spectral resolution can be achieved through precise displacement control of the linear variable monochromatic filter (LVF). By using LabVIEW software to control the system hardware to change its position and triggering the image acquisition system to work synchronously, a sequence of images—a spectral image cube—can be acquired. After acquiring and processing the image signals, detailed information of the micro-area fluorescence spectral scan can be obtained. The microscopic fluorescence spectroscopy analysis software developed based on the NI vision development module can perform relevant image processing and spectral information analysis on the acquired images. The microscopic fluorescence spectroscopy imaging system mainly consists of three functional modules: an LVF-based dispersive system control module, a sequential image acquisition module, and a spectral image processing and analysis module. Dispersive System Control Module The core of a spectrometer is its dispersive system, as the four most important basic characteristics of a spectrometer—working spectral range, dispersion rate, resolution, and light-gathering ability—are all determined by the dispersive system. This system uses a linear variable filter (LVF) from OCEAN Corporation as the dispersive element to achieve spectral blocking or transmission. The software part of the dispersive system mainly utilizes LabVIEW's serial port tool to control the movement of the LVF. Key functions include: ※ System reset ※ Reciprocating motion and specified wavelength position ※ Stepping operation: The motor steps according to a given interval step size, controlled by buttons ※ Stroke control and precise positioning. The control window of the software system is shown in Figure 2. The operation process is as follows: Given a set value, the motor can directly run to the specified wavelength value, and the current wavelength is displayed in real time at the current wavelength. Then, according to the given step value, clicking the step button performs motor stepping. Interruption and reset can be performed at any time as needed. During operation, the busy indicator light control reflects the running information. When the program runs normally, the indicator light flashes, indicating a waiting message; when the program detects an error, the indicator light stops flashing, and the error message bar displays the reason for the error, allowing for timely correction of the error message. [align=center] Figure 2 Control of the Spectroscopic System[/align] Sequence Image Acquisition Module The acquisition of the microscopic sequence images uses a high-performance cold CCD 150CL from Pixera Corporation, equipped with an image acquisition card, but this card lacks a LabVIEW driver. This paper utilizes LabVIEW's DLL dynamic function call function, by calling the control SDK package provided by Pixera Corporation, to implement multiple functions such as exposure time setting, automatic gain adjustment, autofocus, integration time adjustment, black/white balance, color/grayscale switching, and CCD sensitivity setting for image acquisition. This paper designs essential sub-VIs for image acquisition, including CCD driver, image sensitivity, cooling control, and fluorescence fast mode, and automatically saves the image data acquired by the CCD to a temporary file. The ReadFile sub-VI reads the JPEG format image file and displays it in the image display area located on the right side of the panel. In addition to software control, a manual button control is used to control the image acquisition card's CCD, automatically completing image data acquisition, storage, and display. This system is simple to operate, fast, and has strong real-time performance, and can work well with the entire system. Spectral Image Module One of the features of this system is the plotting of microscopic fluorescence spectral curves. As shown in Figure 3, the sequence of images obtained after spectral scanning forms a spectral cube, where images 0 to i correspond to different wavelength values. The wavelength values ​​of the pixels at the center of the image are λ00, λ00, ..., λ0i. For any pixel in each image, its corresponding wavelength and light intensity value can be determined through spectral calculation, thereby realizing the plotting of the spectral curve of any point on the image. [align=center] Figure 3 Spectral Composition of Sequence Images[/align] The following uses FluorCell#2 fluorescent molecular probe micro-fluorescence spectral imaging as an example. First, adjust the imaging illumination, CCD sensitivity, and manual focus under the microscope to select the cell region requiring detailed observation. Then, set the required fluorescence acquisition parameters, wavelength step value, image memory area, and spectral splitting speed for the spectral cube. Start the sequence image acquisition. The system will automatically acquire the corresponding spectral image sequence according to the set wavelength and store it in the specified directory. This directory is automatically set to the current year, month, day, hour, and minute. It can be changed as required after the acquisition is completed. The system interface is shown in Figure 4: [align=center] Figure 4 Spectral Image Acquisition of Fluorcell#2[/align] After completing the image cube acquisition, select a region of interest on an image of any wavelength. Then, through calculation, plot the spectral curve, also known as the "area spectrum," with the median or average intensity of each pixel in the region as the ordinate and the wavelength value as the abscissa, as shown by the white dot in the curve in the lower right corner of the figure above. The selected region will also be marked on the system's display panel. In addition, considering that the number of images in the image cube is limited, it is easy to cause the curve to be uneven. Therefore, we fit the spectral curve using cubic spline interpolation to make the entire curve look smoother, as shown by the red line in the lower right corner of the figure. In addition to the function of spectral curve plotting, the software also adds some basic image processing functions to process a specific image in the image cube. These mainly include: color image display, RGB histogram, RGB threshold segmentation, grayscale image display, 3D image, phase inversion image, image enhancement (image equalization, low-pass filtering, Gaussian filtering, smoothing filtering), edge sharpening (Laplacian), grayscale histogram, grayscale threshold filtering, area calculation and statistics, and line intensity distribution map. Furthermore, to facilitate a comprehensive understanding of the images in the image cube, the system features Flash image display. That is, multispectral images are displayed sequentially at certain time intervals. The effect is similar to animation playback, allowing the observer to better understand the dynamic changes of the target in the image. This method can also be applied to the observation of cell morphological changes. Application Experiment 1. Antiviral Study of Gardenia Extract: Gardenia, a traditional Chinese medicine, is bitter and cold in nature, and enters the heart, lung, and triple burner meridians. It can clear heat and relieve irritability, promote diuresis, cool the blood and detoxify, and has the effect of clearing heat from the triple burner. It is an important medicine in traditional Chinese medicine for treating febrile diseases. Viral infection characteristics: Viral infection depends on the adsorption of virus attachment protein (VAP) to cell surface receptors. Virus attachment protein is an essential pathway to initiate the interaction between the virus and the host cell and establish infection and damage to the cell. The binding of the virus to the cell receptor can be fluorescently labeled. The interaction between cultured human laryngeal cancer epithelial passaged cells Hep-2 and the virus was divided into three groups: (1) Experimental control group y1, only virus was added, no drug was added (2) Adsorption first, then drug added group y2 (3) Drug added first, then adsorption group y3 Images were collected for each group at a time interval of 2 seconds to obtain the time series image group of each group, and the image group was analyzed using a fluorescence spectroscopy analysis system: 1. First, the cell images of the three groups were collected at a time interval of two seconds, and 10 images were collected for each group. 2. Then, the collected images were equalized, smoothed, and Gaussian filtered. 3. The target area was selected on the filtered image, and the selected area and the original image area corresponding to the area were displayed and saved. 4. Threshold filtering was performed on the image to subtract the background information outside the cell. 5. Calculate the pixel area of ​​the cells after threshold filtering. 6. Compare the changes in cell area under the three conditions. [align=center] Figure 5 Microscopic Spectroscopic Analysis of Hep-2 Cell Antiviral Effects[/align] As shown in Figure 5, it can be found that the cell changes are different when only the virus is added and when both the virus and the drug are added. In the former case, there is no obvious change in cell morphology, while in the latter case, the cells show significant changes in morphology and tend to become larger. Therefore, it can be concluded that the addition of gardenia extract has a significant inhibitory effect on the virus. 2. Study on Fluorescent Green Spot Disease of Tea In tea gardens in Rizhao, Linyi and Tai'an, a leaf disease of tea trees is common. Its main symptoms are: abnormal protrusions on the lower epidermis of the leaves, which are green, and the lesions can fluoresce green under light, as shown in Figure 6. [align=center]Figure 6 Green Spot Disease of Tea[/align] Through systematic observation of the disease symptom evolution, it was found that this disease has the characteristic of emitting green fluorescence, and the fluorescence intensifies with the severity of the disease, making it easily distinguishable from the symptoms of other common diseases. Therefore, mastering and utilizing this characteristic can help us correctly identify this disease. We selected a lesion area for fluorescence measurement, as shown in Figure 7. [align=center]Figure 7 Fluorescence Spectrum of Lesion Area[/align] Summary Microscopic fluorescence spectroscopy imaging technology is a commonly used method in microscopic spectroscopy imaging technology. For substances that can produce autofluorescence and excitation fluorescence, microscopic fluorescence spectroscopy imaging technology has significant advantages, including non-invasiveness, visibility, and accuracy. This paper presents a microscopic fluorescence spectroscopy analysis system built based on LabVIEW software, which realizes functions such as digital image acquisition, image processing, and spectral image analysis in biomedicine. The system has a complex functional structure, and all work was completed within one year. Compared with MSDN, it greatly saves development time, and the interface is clean, beautiful, and easy to operate.