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The Research of Feature Extraction Method of Liver Pathological Image based on Multispatial Mapping and Statistical Properties


Affiliations
1 Software College, Northeastern University, Shenyang 110819, China
2 The Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang 110001, China
 

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.
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  • The Research of Feature Extraction Method of Liver Pathological Image based on Multispatial Mapping and Statistical Properties

Abstract Views: 93  |  PDF Views: 2

Authors

Huiling Liu
Software College, Northeastern University, Shenyang 110819, China
Huiyan Jiang
Software College, Northeastern University, Shenyang 110819, China
Bingbing Xia
Software College, Northeastern University, Shenyang 110819, China
Dehui Yi
The Department of Hepatobiliary Surgery, The First Affiliated Hospital of China Medical University, Shenyang 110001, China

Abstract


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.