尿液显微颗粒自动检测与图像处理研究
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摘要
尿液检测是医学临床检验操作规程中的三大常规检验项目之一。我国第三版《全国临床检验操作规程》中对尿液检验提出了明确要求。但是目前很多医院的尿液检验还停留在人工镜检或半自动检验的水平上，不仅速度慢、溯源性差、标准化程度低，而且在很大程度上依赖于检验医师的个人经验。本文结合尿液自动检测的需要，在检测系统的加样机构、智能控制、机器视觉、图像处理与模式识别等方面进行了研究。
在检测系统构成和检测方法方面，设计了全自动多通道在线检测系统，主要包括：加样机构与液路系统、基于ARM的显微镜自动调焦机构和系统软件等；针对原有方法所需自然沉降时间长，而尿液显微颗粒实际上是呈现空间分布的特点，提出了空间动态坐标跟踪检测法，提高了检测速度和检测效果。
在显微图像分析方面，分析了影响图像清晰度的各种因素，设计了一种灵敏度较好的清晰度评价函数，构建了以图像清晰度为控制目标的多传感显微图像自动对焦系统，使得尿液检测系统能够持续稳定地获得高质量的图像；建立了空间多焦面图像融合模型，给出了该模型的算法，设计了基于小波和小波包变换的图像融合方法，通过实验验证了算法的有效性，在避免漏检、少检和错检等保证检测有效性方面取得了较好的效果。
图像显微颗粒的自动分割是尿液自动分析的核心技术，图像分割算法的有效性直接影响显微颗粒形态特征提取的有效性。论文研究了各种图像分割方法及其分割效果，设计了基于索伯尔算子的边缘检测算法；对于图像中的粘连颗粒，引入脉冲耦合神经网络(PCNN)的分割的方法，实验表明，PCNN在处理粘连细胞的欠分割问题上具有良好的效果。
识别准确率是尿液检测系统的主要考核指标，显微颗粒特征提取的性能与有效性直接影响模式识别的准确率。在图像分割的基础上，进一步研究了图像特征的提取方法，从外形、结构、纹理、频域、区域等方面对尿液显微颗粒特征进行了分析，利用BP神经网络对尿液显微颗粒进行分类计数，通过实际测试，证明该神经网络具有良好的分类效果。
论文的研究成果已经应用于苏州惠生电子科技有限公司的全自动尿液自动分析仪上，提高了尿液检测系统的自动化程度，并得到了推广使用。
本文共有图117幅，表29个，参考文献163篇。
Urine microscopic particles detection is one of the three routine examinationitems in clinical medical inspection operating rules. The National Clinical TestRegulation of Operation of third edition had definite requirements for urinemicroscopic particles inspection. But in most hospitals the urine microscopic particlesonly could be inspected by the artificial microscopy method or partial automaticinstruments, which ended in low speed progress, poor measurement traceability andlow standardization, the inspection results was largely dependent on the personalexperience of clinical laboratory physician. Combined with the requirements of urinemicroscopic particles automatic inspection, the adding sample mechanism，intelligentcontrol, machine vision, image processing and pattern cognition of the inspectionsystem were studied in this dissertation.
A fully automatic and multi-channel online detection system was designed on thepart of system configuration and detection method, with such main part as samplemechanism and fluid systems, the microscope automatic focusing system based onARM and system software. Considering the long-time natural sedimentation of oldmethods and spatial distribution characteristic of formal ingredient in urine, thespecial dynamic coordinates track detection method was put forward to improve thedetection speed and detection effect.
As to the microscope imagine processing aspect, different factors affecting theimage resolution were analyzed and a highly efficient resolution evaluation functionwas designed, the multi-sensor microscope image automatic focusing system wasbuilt with image resolution as control objective, which gave the detection system highquality image continuously and steadily.
The special multi-focal plane image fusion model was built and its modelalgorithm was put forward, also with the image fusion method based on wavelet andwavelet packet transform, the algorithm was verified by experiments and proved tohave good effects on eliminating fake positive specimen and ensuring validitydetection.
The formal ingredient automatic segmentation of image is the core technology ofurine microscopic particles automatic detection, the efficiency of image segmentationalgorithm has direct influence on the validity of morphological characteristicsextraction. Various image segmentation algorithm and its effects were investigated inthe dissertation, and edge detection algorithm was designed based on dyadic wavelet and wavelet packet. The Pulse Coupled Neural Network (PCNN) segmentationalgorithm was introduced to deal with binding components in image, and theexperiments indicated that PCNN had good effects to extract binding componentsbetween cells.
Detection accuracy in the main examination index of urine microscopic particlesdetection system, the feature extraction efficiency and performance of formalingredient directly affect the accuracy of pattern cognition. The image featureextraction method was studied on the basis of segmentation, also the urinary formalingredient was classified and counted by BP neural network with its features wereanalyzed from aspects of shape, structure, texture, frequency domain, particulardomain and etc, and the neural network was proved to have good classification effectsafter training and testing.
The research results of this dissertation have been applied to use in FullyAutomatic Urine Microscopic Particles Analysis Instruments of Suzhou HyssenElectronic technology Ltd, thus to improve the automatic level of the urinemicroscopic particles detection and get widely used.
在检测系统构成和检测方法方面，设计了全自动多通道在线检测系统，主要包括：加样机构与液路系统、基于ARM的显微镜自动调焦机构和系统软件等；针对原有方法所需自然沉降时间长，而尿液显微颗粒实际上是呈现空间分布的特点，提出了空间动态坐标跟踪检测法，提高了检测速度和检测效果。
在显微图像分析方面，分析了影响图像清晰度的各种因素，设计了一种灵敏度较好的清晰度评价函数，构建了以图像清晰度为控制目标的多传感显微图像自动对焦系统，使得尿液检测系统能够持续稳定地获得高质量的图像；建立了空间多焦面图像融合模型，给出了该模型的算法，设计了基于小波和小波包变换的图像融合方法，通过实验验证了算法的有效性，在避免漏检、少检和错检等保证检测有效性方面取得了较好的效果。
图像显微颗粒的自动分割是尿液自动分析的核心技术，图像分割算法的有效性直接影响显微颗粒形态特征提取的有效性。论文研究了各种图像分割方法及其分割效果，设计了基于索伯尔算子的边缘检测算法；对于图像中的粘连颗粒，引入脉冲耦合神经网络(PCNN)的分割的方法，实验表明，PCNN在处理粘连细胞的欠分割问题上具有良好的效果。
识别准确率是尿液检测系统的主要考核指标，显微颗粒特征提取的性能与有效性直接影响模式识别的准确率。在图像分割的基础上，进一步研究了图像特征的提取方法，从外形、结构、纹理、频域、区域等方面对尿液显微颗粒特征进行了分析，利用BP神经网络对尿液显微颗粒进行分类计数，通过实际测试，证明该神经网络具有良好的分类效果。
论文的研究成果已经应用于苏州惠生电子科技有限公司的全自动尿液自动分析仪上，提高了尿液检测系统的自动化程度，并得到了推广使用。
本文共有图117幅，表29个，参考文献163篇。
Urine microscopic particles detection is one of the three routine examinationitems in clinical medical inspection operating rules. The National Clinical TestRegulation of Operation of third edition had definite requirements for urinemicroscopic particles inspection. But in most hospitals the urine microscopic particlesonly could be inspected by the artificial microscopy method or partial automaticinstruments, which ended in low speed progress, poor measurement traceability andlow standardization, the inspection results was largely dependent on the personalexperience of clinical laboratory physician. Combined with the requirements of urinemicroscopic particles automatic inspection, the adding sample mechanism，intelligentcontrol, machine vision, image processing and pattern cognition of the inspectionsystem were studied in this dissertation.
A fully automatic and multi-channel online detection system was designed on thepart of system configuration and detection method, with such main part as samplemechanism and fluid systems, the microscope automatic focusing system based onARM and system software. Considering the long-time natural sedimentation of oldmethods and spatial distribution characteristic of formal ingredient in urine, thespecial dynamic coordinates track detection method was put forward to improve thedetection speed and detection effect.
As to the microscope imagine processing aspect, different factors affecting theimage resolution were analyzed and a highly efficient resolution evaluation functionwas designed, the multi-sensor microscope image automatic focusing system wasbuilt with image resolution as control objective, which gave the detection system highquality image continuously and steadily.
The special multi-focal plane image fusion model was built and its modelalgorithm was put forward, also with the image fusion method based on wavelet andwavelet packet transform, the algorithm was verified by experiments and proved tohave good effects on eliminating fake positive specimen and ensuring validitydetection.
The formal ingredient automatic segmentation of image is the core technology ofurine microscopic particles automatic detection, the efficiency of image segmentationalgorithm has direct influence on the validity of morphological characteristicsextraction. Various image segmentation algorithm and its effects were investigated inthe dissertation, and edge detection algorithm was designed based on dyadic wavelet and wavelet packet. The Pulse Coupled Neural Network (PCNN) segmentationalgorithm was introduced to deal with binding components in image, and theexperiments indicated that PCNN had good effects to extract binding componentsbetween cells.
Detection accuracy in the main examination index of urine microscopic particlesdetection system, the feature extraction efficiency and performance of formalingredient directly affect the accuracy of pattern cognition. The image featureextraction method was studied on the basis of segmentation, also the urinary formalingredient was classified and counted by BP neural network with its features wereanalyzed from aspects of shape, structure, texture, frequency domain, particulardomain and etc, and the neural network was proved to have good classification effectsafter training and testing.
The research results of this dissertation have been applied to use in FullyAutomatic Urine Microscopic Particles Analysis Instruments of Suzhou HyssenElectronic technology Ltd, thus to improve the automatic level of the urinemicroscopic particles detection and get widely used.
引文
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