Abstract:Objective To classify the tangible components in urinary sediment images automatically through application of BP (Back Propagation) neural network on basis of Matlab. Methods The urinary sediment images were preprocessed with the methods of graying, histogram enhancement, neighborhood filtering, median filtering and so on. Then, the Canny and Sobel operators were applied to perform edge detection. The information of connected domains for the tangible components were obtained through expansion corrosion and hole filling, from which 12 characteristic values including the perimeter, area, aspect ratio, rectangle and round degree were extracted as the input of BP neural network to classify the tangible components in urinary sediment images. Results The type and quantity of the tangible components in urinary sediment images were obtained with the application of this automatic classification method. Conclusion The automatic classification method made it possible to precisely identify and classify the tangible components in urinary sediment images.
刘肖肖,王兢业. 基于Matlab的尿沉渣图像有形成分的自动分类方法[J]. 中国医疗设备, 2015, 30(2): 29-32.
LIU Xiao-xiao, WANG Jing-ye. Matlab-Based Automatic Classification Method of Tangible Components in Urinary Sediment Images. China Medical Devices, 2015, 30(2): 29-32.