基于改进分水岭算法的菌落图像分割

随着计算机图像处理技术的快速发展，在菌落筛选环节上，传统的人工手动筛选已经逐渐被自动筛选仪器所替代。自动筛选仪器的核心是菌落图像识别模块，而菌落图像识别的关键在于图像分割技术，本文提出了一种改进的分水岭分割算法。该算法首先采用高斯滤波去除图像噪声，再对去噪后的图像进行形态学处理，然后进行倒角距离变换得到菌落距离图像，再采用形态学方法填补其空洞信息，接着对标记后的区域进行分水岭分割，最后利用区域合并算法聚集图像相似区域，从而得到最终的分割图像。采用本文提出的改进型分水岭算法进行菌落图像分割的准确率为93.4%，而传统的分水岭算法的分割准确率为75%，通过与传统分水岭对比实验的结果可以看出，改进后的算法较好地抑制了传统分水岭的过分割现象,极大提高了识别精度。

Colony image segmentation based on improved watershed algorithm

With the development of computer image process technology, traditional manual selecting has gradually been replaced by automatic selecting equipment in the selecting of the colonies. The core of the automatic screening instrument is the colony image recognition module. The key to the colony image recognition lies in the image segmentation technology. This paper proposes an improved watershed segmentation algorithm. The algorithm first uses Gaussian filtering to remove image noise, then performs morphological processing on the denoised image, then performs chamfer distance transformation to obtain the colony distance image, and then fills the cavity information by morphological method, and then performs the labeled region. The watershed is segmented, and finally the region is merged to gather the similar regions, so that the final segmentation image is obtained. The accuracy of colony image segmentation using the improved watershed algorithm proposed in this paper is 93.4%, while the traditional watershed algorithm has a segmentation accuracy of 75%, it can be seen from the comparison with the traditional watershed that the improved algorithm better suppresses the over-segmentation of the traditional watershed and greatly improves the accuracy of recognition.