尿沉渣图像的小波变换与二维最大熵阈值分割算法  

Segmentation algorithm for urinary sediment image combining wavelet transform and 2D-Maximum entropy threshold

作　　者：印勇[1] 赵少敏[1] 

机构地区：[1]重庆大学通信工程学院,重庆400044

出　　处：《重庆大学学报（自然科学版）》2010年第4期92-97,共6页Journal of Chongqing University

基　　金：重庆市自然科学基金资助项目(2006BB3162);重庆大学'211工程'三期建设资助项目(S-09102)

摘　　要：针对尿沉渣图像的复杂散焦以及背景和目标区分度低、有形成分复杂,从而导致尿沉渣有形成分分割困难的问题,提出了一种组合分割方法。首先采用小波变换消除散焦影响,再结合数学形态学方法对图像中目标成分进行定位,分割出子图像,最后利用基于小波变换图像分割和二维最大熵阈值分割的组合分割方法对子图像中不同特点的尿沉渣有形成分分别进行分割,极大的提高了分割的精度。实验结果表明,该方法能够精确有效地实现尿沉渣图像有形成分的分割。In order to solve the problem that urine sediment visible components cannot be segmented effectively because of complex components,complicated defocusing in image and poor discrimination between object and background,a method based on combination algorithm wis designed to segment urine sediment.The wavelet transform wis used to erase the effect of defocusing.Then morphology wis utilized to get the subimages that include the particles.The segmentation method combining the wavelet transform based segmentation and the two-dimensional entropy threshold based segmentation wis employed to segment urine sediment visible components.Experimental results show that the proposed method can segment urinary sediment images effectively and precisely.

关 键 词：尿沉渣有形成分 图像分割 小波变换 数学形态学 二维最大熵 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]