Recognition of Osteoporosis for Mouse Shin in Low Power Microscope based on Multi-feature Fusion
CAI Jie
ZHOU Ke
HE Wenguang
WU Tianxiu
WANG Long
Abstract：For low resolution image, the texture features are more sensitive to direction and distance.An improved method to compute texture features was explored, and combined with shape analysis to raise the accuracy of recognition finally.By analyzing the influence of directions and distances on texture features, we found that correlation and short run emphasis were very sensitive to directions, meanwhile the differences of texture features (based on gray-level co-occurrence matrix) between distances were gradually stable when the distance equalsed to 3.Based on coefficient of variation method, the weighted coefficients were calculated with correlation and short run emphasis.The final weighted texture features were gotten when distance was 3.Combining with shape features, the classifiers of LSVM, KNN, LDA were used to assess Osteoporosis.The weighted texture features showed a higher accuracy than traditional texture features with the use of LSVM, KNN, LDA classifiers, respectively;and the fusion of texture and shape features had a significant improvement in classification with LSVM, KNN, LDA.Meanwhile, the highest recognition accuracy achieved 92.3%.The proposed weighted texture features combining with shape features provide a higher accuracy for recognition of Osteoporosis, and it is helpful to clinical diagnosis.
Keywords：Classification and recognitionCoefficient of variation methodTexture analysisshape analysisOsteoporosis
Publication Date：2017-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：7( 152-158 )