| In recent years, increasingly high-frequency occurrence of red tides in coastal Chinahas seriously affected the safety of drinking water to residents, aquaculture, water landscape value and other aspects, which has caused enormous economic losses every year. The increasingly strong demands of rapid monitoring on the dynamics changes of phytoplankton communities in water and the demands of early warning forecasting of freshwater cyanobacteria blooms, marine diatoms and dinoflagellate red tide have being grown by the government departments and research institutions.In this case, the establishment of a digital and standardized technology platform with a harmful red tides integrated information retrieval, algal species and algal toxins standard supply, identification and detection of standard technology,remote diagnostic services and other features of the harmful red tide diagnostic standard technology platform, has become the country’s pressing needs. Although some international institutions have similar ideas,but such a complete and unique technical support platform is still not built.According to the analysis of HAB occurrence in coastal China, the species list of researchHAB is proposed. Based on the bio-morphological information and multi-viewpoints images about algal species collected in different growth stages, different geographic lines, bio-morphological classification criteria of different angles is obtained. After collecting of algae species and molecular biology, pigment and spectral information, the comprehensive database of HAB of China coastal waters is established. Based on the traditional biological morphological taxonomy, this paper studies a variety of red tide algal identification and detection standards technology system of analytical methods constructed a Web-based harmful red tide biological diagnostic technology platform; and builds microscopic images automatic diagnosis and recognition system based on image analysis, statistical learning and pattern recognition technology with the traditional morphological classification asthe basis,in-depth analysis of algal species characteristics and details of significant differences in shape features.The main work and innovation are as follows:1. Research on the marine biology information and classification of HAB in coastal China. According to the occurrence information of red tide in China’s coastal areas in recent years, identify the issues involved in41species of algal and their ecological taxonomic characteristics and put forward to an idea on ecological taxonomic classification so as to lay a solid foundation on harmful algae database design and microscopic images recognition system.2. Design and implementation of HAB comprehensive database. Form the HAB comprehensive information database by Collecting in different growth stages, and different geographic distribution,marine biology information and different perspectives of the multi-viewpoint images,and brings together the project research information data of the HAB in ecological taxonomic, molecular biology, pigment and optical,etc.Design and create "HAB identification and quantitative detection technology system"database, acquisition the detection technology applied in different spatial and temporal scales and precision.Combined with the application requirements of HAB biological diagnostic technology platform, we design the recognition processing method and identification database for diagnosis, thereby forming a complete comprehensive database of harmful algae.3. Establish HAB biological diagnostic technology platform.The platform includes comprehensive information database, the identification and quantitative detection system, on-line diagnostic system, the virtual center for research and monitoring material supply,which can meet the requests for the input and query platform, for the dynamic releasing relevant information of the research, for the diagnosis and identification of technical interfaces and for the user management."The on-line diagnostic system" integrates a number of modules for project development, including the interactive retrieval between human-computer, microscopic image recognition, chemical classification, three-dimensional fluorescence spectrum recognition, which can provide remote services through the Internet.The platform adopted J2EE architecture and integrated the current mainstream of Struts, Spring, Hibernate and other Web application framework to realize the requests of the system framework of the project. The system design adopted MVC pattern to separate the presentation logic, business logic, database adjustment logic,has very good independence, portability and scalability.4.Research onautomatic identification of HAB microscopic image. Through analyzing of the morphologicaldetail features and shape features of the HAB cells, the automatic classification system of the microscopic images is founded.Effective automaticalextraction separated from three detail features of algae cells (with or without seta,cingulum or sulcus, spine),the important judgement criterion of automatic classification of microscopic images is obtained.Then design three levels of two types of classifiers, and establish three identification system to divide a large sample set into small set. To classifythe different small sample set withcorresponding automatic classification, then make a further extraction of global shape characteristics,so as to get the recognition results. In this way classifier design ideas also improve the recognition accuracy rate.Classifier Ⅰ, according to cells with the seta or not, the first level of classification begins with uploading images. Firstly, the microscopic images of target algaes are extracted out based on gray-scale model algorithm, and according Chaetoceros have more bifurcations, the structures are refined based on morphology to get the details of features of the cytoskeleton of the algae species as the judgement criterion of the chaetoceros. Then the first classification chaetoceros can be diagnosised and return results; the other algaeswithout seta will be going on by using the classifier Ⅱ.Classifier Ⅱ, the algae without seta classification continue with the second level according to the cingulum or sulcus. The maximum contour of target cells are extracted out based on the automatic threshold valuesalgorithm, and then the cingulum or sulcus are extracted based upon the watershed transformwith the constraint mark to get the detailed description of algae cells. To calculate the area ratio between the cingulum and cell and the diantance ratio between the cingulum centroid to cell’s and the length of minimum exterior rectangle of the cell as judgment criterion,then the images with the cingulum or sulcus are classified and can be identified; the images without cingulum or sulcus will be continued by using the classifier Ⅲ.Classifier Ⅲ, regarding the algae without cingulum or sulcus, the classification of the third level begin upon algea withor withoutthe spine by adopting the extraction method based on the best structure element to get the detailed description of the spine of the algae cells anddivide into two categories,then make classification andrecognition.On the classification and recognition, mainly combined with different features of bio-morphological of algae, invariant moments and shape factor features are extracted and described based on the extraction of target cells to form the feature samples; then these samples are trained by using support vector machine to get recognition model database; then identify the sample characteristics with the corresponding model database for pattern recognition; finally obtained good recognition results. We have identification test on41kinds of red tide algae species, a total of3600microscopic images (the training sample2600pieces and test sample1000pieces)by above classifier thought, the average recognition rate is83.27%, removed the three level classifier recognition error, the actual recognition rate average is82.05%, achieved better recognition results. |
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