基于Matlab的尿沉渣图像有形成分的自动分类方法  

Matlab-Based Automatic Classification Method of Tangible Components in Urinary Sediment Images

作　　者：刘肖肖[1] 王兢业[1] 

机构地区：[1]湖北文理学院附属医院(襄阳市中心医院),湖北襄阳441021

出　　处：《中国医疗设备》2015年第2期29-32,共4页China Medical Devices

摘　　要：目的依托于Matlab环境,初步实现BP(Back Propagation)神经网络对尿沉渣图像中有形成分的自动识别与分类。方法首先应用灰度化、直方图增强、邻域滤波和中值滤波等方法对图像进行预处理;再利用Canny算子和Sobel算子叠加处理进行边缘检测,通过膨胀腐蚀和孔洞填充等操作得到有形成分的连通域信息,提取出每个连通域的周长、面积、长宽比、矩形度、圆形度等12个特征值作为BP神经网络的输入;最后利用BP神经网络创建学习训练过程,对每个连通域即有形成分进行分类。结果采用该自动分类方法得到了尿沉渣图像中有形成分的种类和数目。结论该方法分类准确,可实现尿沉渣图像中有形成分的自动识别与分类。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.

关 键 词：尿沉渣图像 边缘检测 特征提取 BP神经网络 MATLAB 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]