In order to realize the detection and analysis of microalgae in sediment samples with complex scenes, the project takes advantage of the character of microalgae that they can auto-fluoresce when exposed to the illumin...In order to realize the detection and analysis of microalgae in sediment samples with complex scenes, the project takes advantage of the character of microalgae that they can auto-fluoresce when exposed to the illumination of certain exciation waves. The project takes grey-scale and fluorescent pictures of microalgae in the same field of view and uses the image processing technique to deal with the images, such as threshold segmentation, contour and texture analysis and pattern recognition. The results show that the fluorescent image can effectively elimate the yawp in the complex background and make the consequent image processing more effective and easy. Then the project comes to the conclusion that fluorescence-assisted image processing can realize the detection and analysis of microalgae in sediment samples containing complex scenes.展开更多
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 method...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.展开更多
文摘In order to realize the detection and analysis of microalgae in sediment samples with complex scenes, the project takes advantage of the character of microalgae that they can auto-fluoresce when exposed to the illumination of certain exciation waves. The project takes grey-scale and fluorescent pictures of microalgae in the same field of view and uses the image processing technique to deal with the images, such as threshold segmentation, contour and texture analysis and pattern recognition. The results show that the fluorescent image can effectively elimate the yawp in the complex background and make the consequent image processing more effective and easy. Then the project comes to the conclusion that fluorescence-assisted image processing can realize the detection and analysis of microalgae in sediment samples containing complex scenes.
基金Supported by the National Natural Science Foundation of China(Nos.61301240,61271406)
文摘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.
