农业工程学报Issue(12)：186-193,8.DOI:10.11975/j.issn.1002-6819.2015.12.025
基于显微图像处理的稻瘟病菌孢子自动检测与计数方法
Automatic detection and counting method for spores of rice blast based on micro image processing
摘要
Abstract
The detection and counting for spores of the rice blast usually relies on the eye observation under a microscope, which is time consuming, labor intensive and inefficient, so an alternative method is required. This paper discussed an innovative method using image processing techniques to detect and count the spores in micro images. Firstly, the micro images of spores were captured with image detection system consisting of a microscope, a video camera, a capturing software and a computer. And then, a correction method was presented to reduce the non-uniform illumination by subtracting the gray value of the background image form the original image. The original image was divided to 4×4 blocks and the gray value of the background was determined by the illumination correction for each dividing part. The spores had strong edge information in the micro images, so the canny operation was applied to do the edge detection. In this process, fuzzy c-means algorithm (FCM) was used to obtain the high threshold of the canny operation automatically in the gradient images. The noises especially for mycelium could be filtered better using FCM-Canny than Ostu-Canny method. Morphological image processing including close and open operations was implemented to fill the spores and filter the noises. According to the differences of the shape characteristics between the spores and the other objects, the features’ combination composed of ellipticity, complexity and width of minimum bounding rectangle was selected after sampling statistics to recognize the spores. When 0.85< ellipticity<1.33, complexity <2.1 and width of minimum bounding rectangle >20, the objects were recognized as the spores, otherwise deleted as noises. The binary images including only spores were gained by a series of image processing, but there were still some adjacent spores in the images. In order to count the spores precisely, these adjacent ones must be separated. This paper presented an improved watershed algorithm (WA) to break the adjacent parts for getting the right number. The binary images of the spores were transformed to the gray images by distance transform (DT), then Gaussian filtering (GF) was applied to unite the redundant local minimum for preventing the over segmentation, and the WA was conducted to separate the adjacent spores at last. To verify the the proposed method, a total of 100 images were collected for the performance evaluation. Experimental results showed that the numbers of the image samples were 79 with detection accuracy of 100%, 16 with detection accuracy from 90% to 100% and 5 with detection accuracy from 80% to 90%. The proposed method achieved high-accuracy detection and counting with average accuracy of 98.5%, which met the requirements of the automatic detection and counting for spores of the rice blast.关键词
图像处理/菌/算法/稻瘟病菌孢子/光照校正/FCM-Canny边缘检测/改进分水岭算法Key words
image processing/bacteria/algorithms/spores of rice blast/illumination correction, edge detection of FCM-Canny/improved watershed algorithm分类
数理科学引用本文复制引用
齐龙,蒋郁,李泽华,马旭,郑志雄,汪文娟..基于显微图像处理的稻瘟病菌孢子自动检测与计数方法[J].农业工程学报,2015,(12):186-193,8.基金项目
国家自然科学基金项目（31101087）；高等学校博士学科点专项科研基金（20104404120002）；现代农业产业技术体系建设专项资金资助（CARS-01-33）；广东省现代农业产业技术体系（粤财教[2009]356号）；浙江省自然科学基金（LQ12C13004）。 （）
