Study On The Image Analysis System Of Urinary Sediment Microscopic Inspection

The urinary sediment inspection is an important methods of clinical diagnostication and one of the common means used in modern medical researching field . However, the traditional optic-microscopical inspection which is watched by one eye is easy to not only increase doctors'labour intensity but also bring up artificial error. Furthermore, the image can't be transformed and processed by this means. It is very difficualty to remote-transmit, make precise measurement quickly and access conveniently the microscopical image. Whereas, all of this can be done by digital image processin technique. Processing and analysis of the urinary sediment's micorscopical image based on computer system can increase efficiency of clinical inspection greatly, relieve the burden of doctors in clinical laboratory, provide standardization of disease diagnosis and manage present & history information of clinic. Moreover, it is easy to share medical datum and realize long-range consultation. The work we do is to researsch a analysis and processing system of urinary sediment microscopic image which will has greater power. The automatic processing and analysis of the visible image of urinary sediment have been discussed in detail on theoretical researching and practical realizing. It is complicated to process this kind of image which has several features such as more targets, complex background, much disturbance and noise, little contrast and so on. We compared and analysed all arithmetics on sediment image processing which includes filtrating, edge picking-up, boundary tracking, image enhancing, image segmenting, character picking-up, feature parameter selecting and so on. This paper put toward a set of effective algorithm to all kinds of urinary sediment. Among them image segmenting is very important that is directly relative to the precision of image analysis. After comparing the results of several segmenting arithmetics, we provide self-adaptation threshold value partition algorithm based on Otsu, which abviously improve the effect of urinary sediment segmenting. To integrallty describe sediment, this paper also extracts some illuminance density and five texture parameters such as energy, square-error, entropy, inertia matrix, correlation coefficient and so on, based on seven morphological parameters which can dicrib the feature of urinary sediment. All of this lay a foundation of the system function's extending.