血浆絮状物无损检测系统设计

检测血浆中是否含有絮状物是保证输血安全的重要措施。针对絮状物人工检测法的缺点,设计基于机器视觉技术和神经网络的血浆絮状物无损检测系统,并对系统设计的关键技术进行研究。基于MATLAB平台设计系统软件以实现图像采集,并采用剪切、反色、中值滤波、灰度切割对图像进行预处理;采用fisher判别方法,结合迭代阈值分割法和标注矩阵连通区域选择法,消除气泡干扰,提取絮状物。系统通过基于BP神经网络建立的识别模型,完成血浆絮状物的判别。临床对比实验结果表明,系统能有效地检测出血浆中是否含有絮状物,具有较好的检测重复性和准确性。每袋样品从图像采集、处理到最终给出检测结果的时间不超过1 min。

Design of nondestructive test system for plasma clots

One of the most important measures that are used to guarantee blood transfusion safety is to detect clots in the plasma before transfusion. To overcome the disadvantages of manual detection method, this research designs a nondestructive testing (NDT) system for plasma clots inspection based on machine vision technique and artificial neural networks. The key technology for system design are studied and presented. Image acquisition is performed by custom-designed software based on MATLAB platform, and the methods of image cut, reverse color, median filter as well as gray cutting are adopted to preprocess image. The use of fisher discrimination method, combined with iterative threshold segmentation method and the selection of connected domain, can successfully eliminate the interference of air bubble and correctly extract the image of plasma clots. Plasma clots are discriminated by a recognition model based on artificial neural network BP algorithms. The results of clinical contrast experiment shows that the system can effectively detect whether plasma contains plasma clots and the new system shows a much higher degree of repeatability and stability. From the image acquisition and processing to the recognition of plasma clots, the detecting time of a sample is no more than 1 min.