The Study Of Key Technology For Full Automatic Urinary Analysis System

The examination of urinary sediment,which is a basic requirement of the operation regulations in the Center for Clinical Laboratories,can be used to distinguish the red blood cell[RBC],white blood cell[WBC],cast,epithelia, crystal,and various physiological or pathological particles in the urine according to morphological,textural,and other features of urinary sediment, and to help the doctors to diagnose diseases of urinary systems accurately. The abnormal changes of urinary systems may not be detected by conventional medical examination or chemical experiments,however,these changes can be distinguished by analyzing the urinary sedimentary components qualitatively and quantitatively.This shows that the examination of urinary sediment has an important clinical application.For the sake of improving the objectivity of the results,enhancing the standardization of the examination,and reducing the labor intensity of staff, it is of great significance to develop a fully automated urine analyzer.The detection methods of urinary sediment visible components are discussed and analyzed in the dissertation.The segmentation and classification of the urinary sediment imaging are discussed deeply by using digital image processing and pattern recognition methods.In terms of the characteristics of the urinary sediment images,a set of complete effective processing scheme for urinary sediment visible components is proposed by combing multi-processing arithmetic with amount of computing and trials.The major tasks of this dissertation are to design and implement the controlling operation and managing software of the urine analyzer,and to perform the algorithms for processing the urinary image and classifying the urinary visible components.In order to accurately analyze urinary visible component,the effective imaging segmentation play a key role.The quality of the images is the foundation of the imaging segmentation.In this dissertation, auto-focus method based on Principle Component Analysis[PCA]is firstly proposed,which can improve the images quality effectively.On the other hand,image preprocessing can decrease the difficulty and increase the accuracy of image segmentation.This dissertation proposes a preprocess algorithm which enhances the edges of objects by stretching the difference between every pixel and the local gray mean value and eliminates the disequilibrium of illumination by nonlinear transform of the local gray mean value.And then,combined with morphological operations and post-process of regions,the cellular neural network[CNN]is employed to get the final segmentation results.By analyzing the initial test results,a layered classifier with multi-BP networks combined is proposed to classify the objects which are described by certain morphological,statistical and textural features.This dissertation is composed by the following six parts:Partâ… :An introduction to hardware system,software system and control flow of the analyzer.Partâ…¡:The pre-processing algorithm to urinary sediment images.An image-enhancement algorithm is proposed,which can be used to enhance the contrast of images by stretching the difference between each point's gray value and the local gray mean value.Furthermore,the uneven illumination and shadow in the image can be eliminated by taking certain nonlinear transform to the gray mean values of the neighborhoods.Partâ…¢:The auto-focus algorithm based on Principle Component Analysis is firstly proposed.Several indices of definition of every image in the same visual field are calculated in advance,and then are integrated by PCA algorithm to evaluate the definition of each image.Partâ…£:Methods of urinary images segmentation.By designing suitable CNN templates and combining with morphological operations,CNN is employed to complete the initial segmentation.And then,post-process linking the opposite points which are conformed by analyzing the shape of the edges is carried out to separate conglutinate cells and eliminate burrs of them.Partâ…¤:The algorithm to extract the features of the regions.By analyzing the image features and their corresponding distribution curves,certain morphological,statistical and textural features are calculated to describe the regions,and therefore prepare for further classification. Partâ…¥:The classification of the urinary sediment components.The basic knowledge of BP Neural Network and Support Vector Machines(SVM)are introduced at first.By analyzing the initial test results,a layered classifiers combination method is proposed.Finally,several BP networks are combined by voting in each layer to improve the accuracy.By combining these methods presented previously,the fully automatic urinary sediment examination is completely implemented.