基于目标颜色基及梯度方向匹配的菌落分割计数算法

【目的】通过菌落测试片提取菌落并计数,在农业、食品业、医疗卫生等领域中是一项常用且重要的工作。目前,菌落自动计数算法大都是以菌落培养皿为主要工作对象,对菌落测试片适用性较差。另外,目前相关技术在常规的粘连物体分割中有着较好的效果,但在菌落分割计数中,由于菌落本身的形态特征,对粘连菌落分割计数的效果尚不够精准。【方法】为解决此类问题,本文提出一种基于目标颜色基及梯度方向匹配的菌落分割计数算法。首先利用图像中菌落的颜色特征作为基,将图像转换到基空间内,以增强菌落与背景之间的差异,其次利用菌落图像的梯度幅值特征对梯度方向进行滤波,然后通过梯度方向进行匹配,进而将粘连的菌落分割,最后利用非极大值抑制的方法筛选出菌落并计数。【结果】经试验,本研究算法的计数精度可达98.00%,能够满足实际需求。【结论】在针对菌落的目标分割计数中,本研究算法不仅计数精度高,而且具有较好的鲁棒性,在对不同厂家的菌落总数测试片菌落分割计数中均有优异效果;然而在对大面积目标的检测分割中算法的准确率会有所下降,因此,该算法更适合于菌落等小目标的检测分割。

Colony segmentation and counting algorithm based on target color base and gradient direction matching

【Objective】 Colony extraction and counting is essential in agriculture, food, and health industries. Currently, most of the available algorithms for automatic counting of colonies use colony culture dishes and has poor applicability to colony count plates. In addition, the current technologies have good performance in conventional segmentation of adherent objects, while their accuracy remains to be improved for the segmentation and counting of adherent colonies due to the unique morphological characteristics of colonies. 【Methods】 To solve such problems, we proposed a colony segmentation and counting algorithm based on target color base and gradient direction matching. Firstly, the color feature of the colony in the image was used as a base to convert the image into a base space to enhance the difference between the colony and the background. Secondly, the gradient magnitude feature of the colony image was used to filter the gradient direction, and then the matching was performed through the gradient direction, thereby segmenting the adherent colonies. Finally, non-maximum suppression was employed to screen and count the colonies. 【Results】 Through experiments, the counting accuracy of the algorithm in this study reaches 98.00%, demonstrating its capability to meet practical requirements. 【Conclusion】 In the context of targeted segmentation and counting of colonies, the algorithm studied in this paper not only exhibits high counting accuracy but also demonstrates good robustness. This algorithm had not only high counting accuracy but also good robustness, producing excellent results in the colony segmentation and counting of colony count plates from different manufacturers. However, it showed decreased counting accuracy in the detection and segmentation of large-area targets. Therefore, this algorithm is suitable for the detection and segmentation of small targets such as colonies.