Objective Real-time fluorescent quantitative polymerase chain reaction (PCR) is the gold standard for nucleic acid detection of infectious diseases and plays an irreplaceable role in many fields, including disease pre...
详细信息
Objective Real-time fluorescent quantitative polymerase chain reaction (PCR) is the gold standard for nucleic acid detection of infectious diseases and plays an irreplaceable role in many fields, including disease prevention and control, clinical diagnosis, food safety, and inspection and quarantine. However, PCR detection encounters significant problems. Changes in the specifications of consumables affect the collection of fluorescent signals. Traditional manual focusing has many drawbacks, including cumbersome operation, frequent calibration, low efficiency, increased labor intensity, and poor image acquisition accuracy. When the consumables change slightly, it is difficult to adapt to multiple consumable specifications. Although there have been research achievements in autofocus technology both at home and abroad, the research conducted on autofocus imaging of fluorescent PCR with multiplespecifications of consumables is still limited. Methods In this study, an optical detection system was constructed for the acquisition of 96-flux fluorescent PCR images. Firstly, the fluorescence imaging situations were observed under different cycle numbers. The performances of evaluation methods, such as the Tenengrad gradient, Laplacian gradient, image variance, Laplacian inhomogeneity, and Sobel operator, were compared during the process of coarse focusing of fluorescent PCR images to determine the appropriate definition evaluation index. Based on the autofocus optical system and the selected definition evaluation function, the image data in the process of the autofocus algorithm were collected, and coarse, fine, and horizontal calibration were performed. After the focusing was completed, a series of image processing operations were performed on the original image in turn, including binarization processing, detection of circular targets by the Hough circle transform, constructing a mask, and calculating the fluorescence intensity by extracting the gray values of the pixels
暂无评论