Extracting the cell objects of red tide algae is the most important step in the construction of an automatic microscopic image recognition system for harmful algal blooms. This paper describes a set of composite methods for the automatic segmentation of cells of red tide algae from microscopic images. Depending on the existence of setae, we classify the common marine red tide algae into non-setae algae species and Chaetoceros, and design segmentation strategies for these two categories according to their morphological characteristics. In view of the varied forms and fuzzy edges of non-setae algae, we propose a new multi-scale detection algorithm for algal cell regions based on border- correlation, and further combine this with morphological operations and an improved GrabCut algorithm to segment single-cell and multicell objects. In this process, similarity detection is introduced to eliminate the pseudo cellular regions. For Chaetoceros, owing to the weak grayscale information of their setae and the low contrast between the setae and background, we propose a cell extraction method based on a gray surface orientation angle model. This method constructs a gray surface vector model, and executes the gray mapping of the orientation angles. The obtained gray values are then reconstructed and linearly stretched. Finally, appropriate morphological processing is conducted to preserve the orientation information and tiny features of the setae. Experimental results demonstrate that the proposed methods can effectively remove noise and accurately extract both categories of algae cell objects possessing a complete shape, regular contour, and clear edge. Compared with other advanced segmentation techniques, our methods are more robust when considering images with different appearances and achieve more satisfactory segmentation effects.
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We would like to sincerely thank the Key Laboratory of Marine Environment and Ecology of the Ministry of Education at Ocean University, China, and the Research Center for Harmful Algae and Aquatic Environment at Jinan University, China, for offering us the samples and equipment needed to observe and capture the optical micrographs of red tide algae used in this study.
Supported by the National Natural Science Foundation of China (Nos. 61301240, 61271406)
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Yu, K., Ji, G. & Zheng, H. Automatic cell object extraction of red tide algae in microscopic images. Chin. J. Ocean. Limnol. 35, 275–293 (2017). https://doi.org/10.1007/s00343-016-5324-6
- non-setae algae
- cell extraction
- non-interactive GrabCut