基于分形维数的中国海常见浮游植物细胞图像特征提取
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摘要
浮游植物作为海洋生态系统中最重要的初级生产者,对海洋生态环境和海洋生物资源影响巨大。以分形理论为基础,本文主要对中国海常见浮游植物细胞显微图像分析和特征提取进行了研究。在深入分析浮游植物细胞生物特征的基础上,尝试使用分形维数对中国海常见浮游植物细胞的形状和纹理特征进行提取。围绕这一问题,本文主要进行了下面五个方面的工作。
在进行灰度图像分形维数计算时,目前广泛使用的是盒维数法,但这种方法计算出的分形维数比较粗糙。针对这一问题,本文提出了一种基于面积覆盖计算分形维数的方法,这种方法所计算出的分形维数几乎是连续的,可以比较精确地反映灰度图像的分形特征。
根据分形维数灰度映射图像的分形性质,本文提出了一种边缘检测方法。应用这种方法对基于面积覆盖法计算得到的分形维数灰度映射图像进行处理后,可以较好地提取出浮游植物细胞的边缘形状,从而有利于同一种浮游植物细胞不同展现形态的划分和不同科属下浮游植物细胞之间的分类。
针对角毛藻细胞图像中角毛边缘轮廓较难提取这一问题,本文对毯覆盖计算分形维数的方法进行了改进。将使用改进后的毯覆盖法计算得到的分形维数灰度映射图像与边缘检测算法相结合,比较完整地提取出了角毛的边缘信息。
为全面地描述角毛藻细胞图像中角毛的分布特征,本文提出了一种灰度图像方向角提取方法,并将其引入到了二值图像分形矢量的计算当中,提出了方向分形矢量的概念。应用方向分形矢量对角毛藻边缘二值图像进行描述,可以体现出角毛的方向信息,为角毛藻属下各种的分类奠定了基础。
根据圆筛藻壳面筛室的排列特点,结合基于分数布朗运动的分形维数计算方法,本文提出了一种圆筛藻特征提取方法。使用这种方法可以较好地描述圆筛藻的壳面纹理特征,为圆筛藻属下各种的分类提供了一种有效途径。
本文通过对以上四种分形维数计算方法的改进,有效地分析、提取和描述出了中国海常见浮游植物细胞的多种生物特征,为下一步中国海常见浮游植物细胞的分类识别奠定了基础。
As the most important primary producer in the marine ecosystems, phytoplankton influences the marine environments and the living marine resources enormously. Based on the fractal theory, the analyse and feature extraction of the phytoplankton cell micrograph are studied in this paper mainly. After analyzing and studying the biologic features of the phytoplankton deeply, fractal dimension is used exploringly to extract the shape and texture features of the cell images of the phytoplankton appearing frequently in China’s sea areas. Around this problem, the following work is done in this paper.
Box-counting method is used widely to estimate the fractal dimension of the gray level image. But the results computed by the method are so coarse that they could hardly be used as the feature for image analysis. So a new fractal dimension estimating method based on area covering is proposed in this paper. The fractal dimensions estimating by this method are continuous almost, which can depict the fractal features of the gray level image accurately.
According to the fractal character of the fractal dimension gray map image, an edge detection method is proposed. The edge shape of the cell of the phytoplankton can be detected using this method well from the fractal dimension gray level mapping images obtained by area covering method. The edge can be used to differentiate the different states of the same alga or to classify the alga belong to different family or genus.
The edges of the setae in the Chaetoceros cell images are difficult to detect. The blanket method is improved in this paper to solve the problem. The edges of the setae can be detected perfectly by processing the fractal dimension gray level mapping images, which are estimated by improved blanket method, using the proposed edge detection method.
For depicting the distributing feature of the Chaetoceros cell setae roundly, an image orientation angle estimating method is proposed. Then a new conception named orientate fractal vector is proposed by introducing the orientation angle into the computation of the fractal vector of the binary image. The orientate fractal vectors are good features for describing the orientate information of the setae, which are benefit for the classification of the Chaetoceros cells.
According to the arrangement characteristic of the lucolus on the Coscinodiscus valve, a method of Coscinodiscus features extraction is proposed by using the fractal dimension estimating method based on fractional brown motion. The valve texture features can be described well using this method, which is useful for the classification of the Coscinodiscus cells.
By improving the four methods for estimating the fractal dimension, many kinds of the biologic features of the phytoplankton are analyzed ,extracted and described effectively, which lays a foundation for the classification and the recognition of the phytoplankton appearing frequently in China’s sea areas.
在进行灰度图像分形维数计算时,目前广泛使用的是盒维数法,但这种方法计算出的分形维数比较粗糙。针对这一问题,本文提出了一种基于面积覆盖计算分形维数的方法,这种方法所计算出的分形维数几乎是连续的,可以比较精确地反映灰度图像的分形特征。
根据分形维数灰度映射图像的分形性质,本文提出了一种边缘检测方法。应用这种方法对基于面积覆盖法计算得到的分形维数灰度映射图像进行处理后,可以较好地提取出浮游植物细胞的边缘形状,从而有利于同一种浮游植物细胞不同展现形态的划分和不同科属下浮游植物细胞之间的分类。
针对角毛藻细胞图像中角毛边缘轮廓较难提取这一问题,本文对毯覆盖计算分形维数的方法进行了改进。将使用改进后的毯覆盖法计算得到的分形维数灰度映射图像与边缘检测算法相结合,比较完整地提取出了角毛的边缘信息。
为全面地描述角毛藻细胞图像中角毛的分布特征,本文提出了一种灰度图像方向角提取方法,并将其引入到了二值图像分形矢量的计算当中,提出了方向分形矢量的概念。应用方向分形矢量对角毛藻边缘二值图像进行描述,可以体现出角毛的方向信息,为角毛藻属下各种的分类奠定了基础。
根据圆筛藻壳面筛室的排列特点,结合基于分数布朗运动的分形维数计算方法,本文提出了一种圆筛藻特征提取方法。使用这种方法可以较好地描述圆筛藻的壳面纹理特征,为圆筛藻属下各种的分类提供了一种有效途径。
本文通过对以上四种分形维数计算方法的改进,有效地分析、提取和描述出了中国海常见浮游植物细胞的多种生物特征,为下一步中国海常见浮游植物细胞的分类识别奠定了基础。
As the most important primary producer in the marine ecosystems, phytoplankton influences the marine environments and the living marine resources enormously. Based on the fractal theory, the analyse and feature extraction of the phytoplankton cell micrograph are studied in this paper mainly. After analyzing and studying the biologic features of the phytoplankton deeply, fractal dimension is used exploringly to extract the shape and texture features of the cell images of the phytoplankton appearing frequently in China’s sea areas. Around this problem, the following work is done in this paper.
Box-counting method is used widely to estimate the fractal dimension of the gray level image. But the results computed by the method are so coarse that they could hardly be used as the feature for image analysis. So a new fractal dimension estimating method based on area covering is proposed in this paper. The fractal dimensions estimating by this method are continuous almost, which can depict the fractal features of the gray level image accurately.
According to the fractal character of the fractal dimension gray map image, an edge detection method is proposed. The edge shape of the cell of the phytoplankton can be detected using this method well from the fractal dimension gray level mapping images obtained by area covering method. The edge can be used to differentiate the different states of the same alga or to classify the alga belong to different family or genus.
The edges of the setae in the Chaetoceros cell images are difficult to detect. The blanket method is improved in this paper to solve the problem. The edges of the setae can be detected perfectly by processing the fractal dimension gray level mapping images, which are estimated by improved blanket method, using the proposed edge detection method.
For depicting the distributing feature of the Chaetoceros cell setae roundly, an image orientation angle estimating method is proposed. Then a new conception named orientate fractal vector is proposed by introducing the orientation angle into the computation of the fractal vector of the binary image. The orientate fractal vectors are good features for describing the orientate information of the setae, which are benefit for the classification of the Chaetoceros cells.
According to the arrangement characteristic of the lucolus on the Coscinodiscus valve, a method of Coscinodiscus features extraction is proposed by using the fractal dimension estimating method based on fractional brown motion. The valve texture features can be described well using this method, which is useful for the classification of the Coscinodiscus cells.
By improving the four methods for estimating the fractal dimension, many kinds of the biologic features of the phytoplankton are analyzed ,extracted and described effectively, which lays a foundation for the classification and the recognition of the phytoplankton appearing frequently in China’s sea areas.
引文
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