Automatic cell object extraction of red tide algae in microscopic images

• Published in: Chinese journal of oceanology and limnology 2017-03, Vol.35 (2), p.275-293
• Main Author: 于堃 姬光荣 郑海永
• Format: Article
• Language: English
• Subjects: Algae; Algal blooms; Algorithms; Biology; Cellular biology; Chaetoceros; Detection; Earth and Environmental Science; Earth Sciences; Eutrophication; Feature extraction; Image processing systems; Marine; Morphology; Object recognition; Oceanography; Orientation; Physical characteristics; Plankton blooms; Red tide; Red tides; Segmentation; Setae; Shape; 图像识别系统; 形态学操作; 显微图像; 灰度信息; 目标提取; 自动分割; 藻细胞; 赤潮生物
• ISSN: 0254-4059; 2096-5508; 1993-5005; 2523-3521
• DOI: 10.1007/s00343-016-5324-6

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 ofnon-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 multi- cell 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.