Study On The Shape And Texture Features Of Image Pattern And Their Application In The Recognition Of Urinary Sediment Visible Components

The automatic recognition of urinary visible components is of great significance in clinical examination of urinary sediment. The traditional manual microscopic examination method is not only labor intensive, sensitive to subjective factors, but also make against with rapid and accurate quantitative diagnosis as it is mainly centralize on the qualitative examination of visible components. With the development of digital image processing and pattern recognition technology, the computer-aided analysis of urinary sediment microscopic images has become possible. The invention of automatic urinary sediment analyzer can not only greatly improve the efficiency of clinical examination, reduce the labor intensity of physicians, but also provide help on hospital's informationization, standardization of disease diagnosis, and facilitate the sharing of health care resources and remote consultation.Several automatic urinary sediment analyzers have emerged in markets. The automatic process of urinary sediment images is commonly divided into segmentation, recognition and counting. For recognition part, the commonly used method is extracting shape and texture features and use classifier for classification. At present, the shape features used for recognition are usually some simple features, such as area, perimeter and circular degree and so on, and the used texture features are usually extracted in spatial domain. Based on the previous research, some new shape description methods are proposed according to the special shape features of some kinds of visible components in urinary sediment microscopic images. And for the research of texture features, as the high frequency wavelet coefficients have special advantages for texture feature representation, a texture recognition method based on the wavelet domain statistical texture features is proposed.For image segmentation: the common used image segmentation methods are introduced, and a gray variance based bi-thresholding urinary sediment image segmentation method and a watershed based overlapped cells'segmentation method are introduced. Using the snake model for cells'boundary location, and using the edge of binary image after dilation as it's initial contour.For shape features and their applications: the common used shape description methods and centerline extraction methods are introduced. The distance transform based centerline extraction method is improved for the extraction of single pixel wide, connected, and no branch centerline of casts, and based on the extracted centerline, a tube-like shape description method for casts recognition is proposed. Combined with other methods and using decision tree as classifier, a shape recognition method of casts is proposed. The theory of Hough transform and its implementation are studied, using the Hough transform based line detection method for the recognition of crystal, and using the Hough transform based circle finding method for distinguishing between white blood cell clusters and epithelial cells, and for the segmentation and counting of white blood cells. The Hessian matrix based vessel enhancement method is applied to the enhancement of sperm image, combined with Otsu binarization and region growing method, a head locate and tail tracing method for sperm recognition is proposed.For texture features and their applications: The common used statistical texture feature extraction methods are introduced, including moment characteristics, spatial autocorrelation function, GLCM, and so on. A wavelet domain statistical texture feature based texture classification method is proposed and applied to the texture recognition of visible components in urinary sediment images.