摘要
通过融合浮游植物藻类细胞显微明场图像和荧光图像对浮游植物进行鉴定可以有效提高浮游植物鉴定的精度,然而在图像采集过程中存在明场图像与荧光图像中藻细胞位置不匹配的问题。为此,本文以刚体变换作为明场图像与荧光图像的空间变换模型,以明场HSV颜色空间S通道二值化图像与荧光灰度二值化图像的归一化互信息作为明场图像与荧光图像的相似度,利用粒子群优化算法对小波五级分解的低频分量进行粗配准,然后将初步配准的平移量和旋转角度作为初始值,利用鲍威尔算法对小波三级分解的低频分量进行配准精度微调。栅藻、羊角月牙藻和念珠藻的实验结果表明:对明场图像与荧光图像进行处理后的归一化互信息具有明显的峰值,可以更好地表征浮游藻细胞明场图像与荧光图像的相似度;将粒子群优化算法与鲍威尔算法结合的多分辨率图像配准方法,对栅藻、羊角月牙藻、念珠藻显微明场图像与荧光图像配准的误配率分别为0、9.4%、6.5%,平均配准时间分别为10.43、27.98、17.02 s,配准后的归一化互信息分别为0.673、0.495、0.631。研究结果验证了所提方法在配准精度、运行时间等方面的优势,为融合显微荧光图像和明场图像进行浮游植物鉴定奠定了基础。
Objective Detection of phytoplankton diversity is an important part of a water quality bioassessment.The traditional m anual microscopic detection of algae species requires professional operation and is time-consuming and laborious;therefore,these challenges can be overcome by the development of a method for automatic identification of phytoplankton cell images.Similar to manual identification,deep learning and other automatic identification technologies identify phytoplankton cells based on the morphological characteristics of bright field cell images;however,the practical applications of such technologies encounter complications,such as the difficulty of accurate segmentation of phytoplankton cells and the limitation in high recognition accuracy of algae species only with a small range of groups.Previous studies have shown that the accuracy of algal cell segmentation and recognition can be effectively improved by fusing the bright field and fluorescence images of phytoplankton cells.However,the fusion of bright field and fluorescence images synchronously measured from phytoplankton cells requires significantly high accuracy and shockproof capability of the mechanical structure of the acquisition system.Under a high-power microscope,even a small error in the mechanical structure or a small vibration of the camera can lead to a dislocation between the bright field and fluorescence images,thus causing difficulties in the fusion of the images.Therefore,the registration of bright field and fluorescence images of algae is of great significance for the automatic identification of phytoplankton.Methods The displacement between bright field and fluorescence images can be represented by a rigid transformation m odel,which consists of three parameters:the translation in the x direction,translation in the y direction,and rotation angle.Normalized mutual information is used to calculate the similarity between the bright field and fluorescence images.The goal of image registration is to find a set of rigid transformation parameters that maximize the normalized mutual information of the two images.Owing to the significant difference between the bright field and fluorescence images of phytoplankton cells,the similarity is difficult to be characterized by directly calculating the normalized mutual information.In this study,the normalized mutual information of the S channel binary image of the bright field HSV color space and the binary image of the fluorescence gray level was assumed as the similarity between the bright field and fluorescence images.The S component of the bright field HSV image and the fluorescence gray image were decomposed using a two-dimensional discrete wavelet transform,and the low-frequency components were binarized,to accelerate the registration.First,the particle swarm algorithm was used to register the low-frequency components of the five-level wavelet decomposition.Subsequently,the translation and rotation angle of the preliminary registration were assumed as the initial values,and the low-frequency components of the wavelet three-level decomposition are further registered using Powell’s algorithm.Results and Discussions Scenedesmus sp.,Selenastrum capricornutum,and Nostoc sp.are used as experimental o bjects.The similarity and registration methods are compared and analyzed.As shown in Figs.3 and 4,the normalized mutual information of the bright field S channel and the fluorescence grayscale image has an obvious peak value after binarization,which is extremely conducive to parameter optimization in the registration process.As shown in Fig.5,after the bright field and fluorescence images are decomposed by the wavelet,the noise in the high-frequency component is concentrated,and the low-frequency component has a higher normalized mutual information.Therefore,the normalized mutual information of binary images of low-frequency components after wavelet decomposition is chosen as the similarity measurement index for bright field and fluorescence images.Table 1 presents an experimental comparison between the proposed method and other registration methods.Compared with particle swarm optimization algorithm,the proposed method reduces the mismatch rate by 0,8.1,and 0.7 percentage points,shortens the running time by 128.26 s,448.95 s and 237.20 s,and improves the normalized mutual information by 0.065,0.083,and 0.106;Powell’s method d epends on the initial value;therefore,it is easy to fall into the local maximum in the process of optimization,which leads to a high mismatch rate;compared with GA,the mismatch rates of proposed method are reduced by 6.1,26.2,and 10.2 percentage points,the running time is shortened by 23.78 s,60.95 s and 33.74 s,and the normalized mutual information after registration is improved by 0.149,0.170,and 0.180.The experimental results demonstrate that the proposed method has obvious advantages in terms of registration accuracy and running time compared with other registration methods.Conclusions In this study,the bright field image is converted into a binary image by processing the S channel of the H SV color space;the fluorescence gray image is binarized;normalized mutual information is used as the similarity criterion to characterize the similarity of the bright field and fluorescence image.Using Scenedesmus sp.,Selenastrum capricornutum,and Nostoc sp.as experimental objects,the application of wavelet decomposition in the registration of microscopic bright field and fluorescence images of phytoplankton cells was studied.The global search advantage of the particle swarm optimization algorithm is used for preliminary registration in the high-level decomposition component,and the local search ability of Powell algorithm is used for fine-tuning the registration accuracy in the low-level decomposition component.A comparative analysis is performed with other commonly used registration methods,and the experimental results verify the feasibility of the registration method.
作者
贾仁庆
殷高方
赵南京
徐敏
胡翔
黄朋
梁天泓
何前锋
陈晓伟
甘婷婷
张小玲
马明俊
Jia Renqing;Yin Gaofang;Zhao Nanjing;Xu Min;Hu Xiang;Huang Peng;Liang Tianhong;He Qianfeng;Chen Xiaowei;Gan Tingting;Zhang Xiaoling;Ma Mingjun(School of Environment Science and Optoelectronic Technology,University of Science and Technology of China,Hefei 230026,Anhui,China;Key Laboratory of Environmental Optics and Technology,Anhui Institute of Optic and Fine Mechanics,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,Anhui,China;Hefei University,Hefei 230601,Auhui,China;Anhui Hefei Ecological Environment Monitoring Center,Hefei 230088,Anhui,China;Anhui University,Hefei 230601,Anhui,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2022年第24期80-87,共8页
Chinese Journal of Lasers
基金
安徽省科技重大专项(202203a07020002,202003a07020007)
国家自然科学基金(61875207,62005001)
深圳市可持续发展科技专项(KCXFZ20201221173007020)。
关键词
生物光学
浮游植物
明场图像
荧光图像
图像配准
互信息
bio-optics
phytoplankton
bright field image
fluorescence image
image registration
mutual information
