The Research of Feature Extraction Method of Liver Pathological Image Based on Multispatial Mapping and Statistical Properties.
The Research of Feature Extraction Method of Liver Pathological Image Based on Multispatial Mapping and Statistical Properties.
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基于多空间映射和统计特性的肝脏病理图像特征提取方法研究
DOI：
10.1155/2016/8420350
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发表时间：
2016
影响因子：
--
通讯作者：
Yi D
中科院分区：
文献类型：
--
作者：
Liu H;Jiang H;Xia B;Yi D
提出了一种基于多空间映射和统计特性的肝脏病理图像特征提取方法。对于苏木精-伊红染色的肝脏病理图像，R和B通道图像能更好地反映肝脏病理图像的敏感性，而熵空间和局部二值模式(LBP)空间能更好地反映图像的纹理特征。为了获得更全面的信息，我们将肝脏病理图像映射到熵空间、LBP空间、R空间和B空间。针对传统的高阶局部自相关系数(HLAC)不能反映图像整体信息的问题，提出了一种平均修正的HLAC特征。通过计算病理图像的统计特性和平均灰度值，将当前像素值更新为当前像素值与平均灰度值之差的绝对值，从而对病理图像的灰度值变化更敏感。最后，使用HLAC模板计算更新后图像的特征。实验结果表明，改进后的多空间映射特征对肝癌具有更好的分类性能。
We propose a new feature extraction method of liver pathological image based on multispatial mapping and statistical properties. For liver pathological images of Hematein Eosin staining, the image of R and B channels can reflect the sensitivity of liver pathological images better, while the entropy space and Local Binary Pattern (LBP) space can reflect the texture features of the image better. To obtain the more comprehensive information, we map liver pathological images to the entropy space, LBP space, R space, and B space. The traditional Higher Order Local Autocorrelation Coefficients (HLAC) cannot reflect the overall information of the image, so we propose an average correction HLAC feature. We calculate the statistical properties and the average gray value of pathological images and then update the current pixel value as the absolute value of the difference between the current pixel gray value and the average gray value, which can be more sensitive to the gray value changes of pathological images. Lastly the HLAC template is used to calculate the features of the updated image. The experiment results show that the improved features of the multispatial mapping have the better classification performance for the liver cancer.