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Research and Realization on Feature Extraction Technology and ID3 Recognition Algorithm on Cell Image

Author: TangYuPeng
Tutor: LinJiaYu；LiangGuangMing
School: National University of Defense Science and Technology
Course: Information and Communication Engineering
Keywords: Automatic identification Feature Extraction Corner detection Unified Hu Moment Texture Spectrum Decision Tree ID3 Balanced
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 91
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Abstract


Automatic inspection and identification of the cell image is a hot field of biomedical engineering problem of great theoretical and practical significance , and can improve the efficiency of the medical examination , the doctor was freed from the complex repetitive work , and to get rid of the knowledge and experience of the inspection dependence , and increase the authority of the inspection . Papers around the cell image feature extraction technology and the decision tree automatically identify ID3 algorithm , mainly to complete the following tasks : (a ) in a study traditional shape feature extraction methods based on corner detection based on the image boundary polygonal approximation of shape feature extraction algorithm defines the the boundary gradient histogram mean square gradient direction of the gradient in the value of the area ratio of the threshold , the corner points and the number of vertices than the four-dimensional characteristics describe the tube -type crystalline cell boundary characteristics . (B) The proposed new approach to a fusion of the texture spectrum image enhancement technology and unified Hu moment invariant features extracted . The experiments show that the extracted texture features the good areas dividend cells and white blood cells . (C ) for the sample data distribution and little prior knowledge , based on the characteristics of histogram equalization data normalization method , you can convert the sample data distribution law for uniform distribution , enhance the separability of the data , to speed up Decision tree learning speed . ( 4) using C - means clustering algorithm to achieve the ID3 decision tree algorithm , can eliminate the tendency to select values ??more characteristic ID3 algorithm , to improve the efficiency and the precision of the decision tree learning . ( E) of the extract 25 - dimensional cell image shape and texture features signature database , automatic cell image recognition , the accuracy and speed of the system can meet the actual demand , and has been used in clinical practice . The research results applied to the cells in the Automatic Identification System , the experiments show that the system recognition rate is improved .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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