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

Autor: Huiling Liu, Huiyan Jiang, Bingbing Xia, Dehui Yi
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Cytoplasm
Article Subject
Local binary patterns
Feature extraction
02 engineering and technology
lcsh:Computer applications to medicine. Medical informatics
General Biochemistry
Genetics and Molecular Biology
Pattern Recognition
Automated
03 medical and health sciences
0302 clinical medicine
Image Processing
Computer-Assisted
0202 electrical engineering
electronic engineering
information engineering
Humans
Entropy (information theory)
False Positive Reactions
Computer vision
Pathological
Mathematics
Principal Component Analysis
Models
Statistical
General Immunology and Microbiology
Pixel
business.industry
Applied Mathematics
Liver Neoplasms
Autocorrelation
Reproducibility of Results
Pattern recognition
General Medicine
Image Enhancement
Liver
030220 oncology & carcinogenesis
Modeling and Simulation
Principal component analysis
lcsh:R858-859.7
020201 artificial intelligence & image processing
Artificial intelligence
business
Algorithms
Image based
Research Article
Zdroj: Computational and Mathematical Methods in Medicine, Vol 2016 (2016)
Computational and Mathematical Methods in Medicine
ISSN: 1748-670X
DOI: 10.1155/2016/8420350
Popis: 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.
Databáze: OpenAIRE
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