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改进FCM和LFP相结合的白细胞图像分类

DOI: 10.11834/jig.20130508

Keywords: 白细胞分类,图像分割,模糊C-均值聚类,纹理特征提取,局部模糊模式

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Abstract:

研究白细胞图像分类识别中有效的图像分割与特征提取方法,以提高白细胞图像的正确识别率。由于某些白细胞(粒细胞)中颗粒的存在,严重影响细胞核与细胞质区域的正确分割,通过将空间信息与核函数融入模糊C-均值聚类(FCM)算法,提出一种改进的FCM算法。应用该算法对白细胞图像进行分割,并采用数学形态学方法对分割后的图像进行处理,获得了很好的分割效果,解决了粒细胞的质核分割难题。对于细胞的纹理特征提取,通过对局部二值模式(LBP)中阈值参数的模糊化,建立了基于局部模糊模式(LFP)的纹理特征提取算法。运用本文方法进行图像分割和纹理提取,以支持向量机作为分类器,对CellAtlas的100幅白细胞图像进行了分类识别的实验,结果表明白细胞的正确识别率达到93%。

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