小波分析用于菌落图像的纹理分割  

COLONY IMAGES TEXTURE SEGMENTATION BASED ON WAVELET ANALYSIS THEORY

作　　者：王积分[1] 赵增华[1] 舒炎泰[1] 

机构地区：[1]天津大学计算机系,天津300072

出　　处：《计算机研究与发展》1999年第12期1467-1471,共5页Journal of Computer Research and Development

基　　金：国家自然科学基金

摘　　要：微生物菌种是发酵工业的基础与核心，筛选和改良菌种有利于稳定和降低成本．菌落的形貌特征是菌种生化特性的宏观表象，一直是实际工业生产中人工筛选的主要判定依据．用计算机视觉代替人眼，实现菌种的自动分类与识别，能极大地提高生产效益．文中以“绿僵菌”为例，基于小波分析理论，实现了“绿僵菌”菌落显微图像的纹理分割．该算法首先对原始图像实施二维多分辨率小波分解，得到特征图像；然后对其直方图进行一维小波变换，多阈值选择，生成区域图像．实验表明，通过调整参数：一维小波分解级数Ｓ和模糊阈值β，能得到图像的最佳纹理分割，为菌种的自动分类与识别提供了有效信息．Microbial germ is very important in fermentation industry, which is always selected and optimized in order to reduce costs. The appearance of the colony is related to its biochemical performance, and is helpful to manual selection in industrial production. If computer vision instead of man eyes is applied to germ classification and recognition, the efficiency will be improved obviously. In this paper, an algorithm for colony image texture segmentation is presented, taking green muscardine fengus as an example. The approach is based on the wavelet analysis theory. Firstly, the original image is decomposed into feature images with 2D multiresolution wavelet representation. Then, these feature images are segmented into intermediate region images, employing 1D wavelet transforming and multithresholding technique. Finally, as the result shows, by selecting the two parameters(the scale S of 1D wavelet decomposition, and the ambiguous threshold β ), the image can be segmented very well. Especially useful information is provided for colony auto\|selection.

关 键 词：微生物菌种 发酵 小波分析 纹理分割 菌落图像 

分 类 号：TQ920.1[轻工技术与工程—发酵工程] TP391.41[自动化与计算机技术—计算机应用技术]