The Research Of Urinary Sediment Visual Component Analysis Based On Fuzzy Clustering

Urine sediment image checking as the important evidence clinical pathologic analysis has become a key topic of medical research discussion. Urine visual component checking is very essential because it is incredible checking tool in urine analysis which is not only clinically significant to clinic diagnose, remedial monitoring and health investigation, but also playing an important role in diagnose and differentiation of kidney diseases, urinary tract diseases, circulatory system diseases and infectious diseases. Therefore, the veracity and speed of urine sediment image checking and visual component analysis is the key point to urine sediment image research. Obviously, traditional manual naked eye checking definitely has higher veracity. Despite the huge mount of urine specimens in every large hospital, multiple types are in urine sediment image, what is worse, each of objectives are also complicated. Such huge problem can be settled by urine sediment analyzer and digital image processing.Nowadays, automatic urine sediment analyzer is a high intelligent, automatic, objectively based on PC micro image urine visual component analyzer. It is a precise apparatus system combining PC technology, precise mechanical technology, and optical micro imaging technology, auto control technology, digital image processing technology and visual machine technology. Among all these above, digital image processing and understanding is one of the key points. There are multiple methods in urine sediment visual components model identification, such as ANN, SVM, Bayers and etc. But none of these is exceptionally adopting the pattern of linear recognizing method.The method abandoned reorganizing every objective is first pick 5 shape traits and 12 texture traits of objectives. Depending on these trait parameters, gather all objectives into several sorts using fuzzy clustering. Clustering Analysis is a typical data explore and analysis method. Use F-statistic to determine the final clustering. After clustering, use Otsu to take out the elements with low similarity and take them into indeterminate set, because impurity in each sort is inevitable. Thus, we guarantee the veracity determination correctness and raise the veracity by re-identifying the uncertain elements. Then, put the sampling elements from each sorts through the ANN to give the decided component name to each sorts. The elements will decide which sort is red cell and which white cell is. At last, use ANN to re-identify those uncertain elements to raise veracity.The method is proved valid and effective by experiments and data. As to the elements with greater discrepancy in area veracity is high, such as casts and epithelial cells. It is also easy to distinguish calcium oxalate crystals, but ordinary effort in smaller discrepancy cells.