Study On Urinary Sediments Visible Component Automation Classification System

In order to solve the default of using microscope observation and visual method determine the nature analysis urinary sediment smear and carrying manually analyze, identification and count according to color or form characteristic of the visible urinary sediment component, a set of urinary sediments visible component analysis system including pathology analysis, classification and diagnosis is proposed in this paper. In actual application, the system can make statistics and analyses rapidly and accurately which not only release the doctor workload, but improve work efficiency and diagnose correct rate and have important signification to analyzes urinary sediment pathology diagnosis.This paper researches optimization detection method of urinary sediment image visible component from theory and practice. It discusses deeply the segmentation and classification using digital image processing and pattern recognition theory. Through amount of computing and experiments, a set of complete effective processing scheme for urinary sediment visible component is proposed by combining multi-processing arithmetic from the characteristic of urinary sediment image.It is necessary to correctly segment image in order to analyze accurately urinary sediment visible component. This paper adopts watershed transform to segment those images, but this method easily products the over segmentation which results in the edge line buried in disorderly watershed line. In order to overcome that default, morphologic transform and signature extracting are used before watershed transform and used region merging method to remove or decrease the question of over segmentation. However, the over segmentation is still exist. In order to overcome this question, a kind of method which combines level set and watershed transform is proposed. It is proved by test that the proposed method not only resolve the question of over segmentation but has the ability accurately and speed to segment urinary sediment visible component. And then, using morphology feature parameter analyzes and describes segmented urinary sediment image. Using contour tracking, RBFNN and SVM three kind of different method classify urinary sediment visible component and using feature parameter value which is computed by contour tracking method input SVM as input train sample. Through reply train and test, the multi-classification is get which resolves the question of multi-classification of urinary sediment visible component. The author think that the creative work in this paper is as following:(1) Based on the theory and test research, the author has pointed out over segmentation question when using watershed algorithm segments sediment image. In order to solve that question, morphologic transform are used to pre-proposed image to remove...