Research On Microscopic Image Identification Of Harmful Algal Blooms Based On Fractal Method

In recent years, following the aggravation of the ocean environment pollution and water eutrophication, harmful algal blooms occur more and more frequently, which bring serious disaster and loss. It caused more attention by the governments, the public and scientists. Our country arranges researches on formation mechanism, early warning, forecasting, preventing and controlling methods of red tides on different aspects, such as the fundamental research and high technology development, which aims to establish operational monitoring system of red tides. In automatic monitoring of the red tide, identifying the dominant species of red tide rapidly and effectively is an important component.Fractal theory is a very active mathematics branch in non-linear domain, and it is widely applied in image processing in recent years. Fractal encoding has been rapid developed because of its advantage of high compression, meanwhile according to the attractor invariant features of fractal encoding, we can use the fractal neighbor distance for image matching and then achieving the purpose of identification. The fractal neighbor distance is a similarity measure for the encoded images by fractal encoding.This topic comes from the National High Technology Research and Development Program (863 Program)"Research on the diagnosis system for biology of harmful algal blooms"(Grant No. 2006AA09Z178). According to the situation that harmful algal blooms appear in China's coastal waters, acquiring the multi-viewpoints microscopic images of harmful algal blooms on different growing periods and different angles. Based on fractal theory, this paper studies the identification of the microscopic images of harmful algal blooms after object extraction. This paper mainly contains the following work: 1. In the process of image acquisition, the background of the microscopic images becomes extremely complex because of the algae cells are surrounding of the sand, noise and impurities. It will affect the following automatic identification. Aiming at the disadvantage, proposing an object extraction method for microscopic images based on the Otsu method and maximum contour extraction technology. This method can extract the object cell from the microscopic images well, which is useful for the species without setae. It can separate the object cell information in microscopic images. Besides, locating the cell in microscopic images accurately by extracting the maximum contour to remove the sand, noise and impurities. This work lays the foundation for further identification.2. Fractal encoding algorithm is time-consuming, this disadvantage lead it can't be widely applied. It is possible that the limitation on the compression speed has been offset by the advantages of fractal encoding algorithm. Aiming at the disadvantage, we adopt the improved fractal encoding method based on the variance to reduce the searching numbers of the range blocks looking for the best matching blocks, thereby improving encoding speed and the quality of the decoded images. Therefore, forming fractal encoding library of the microscopic images by using the improved fractal encoding method, and then making the identification work based on fractal neighbor distance. And the experimental results prove the feasibility of this method.