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《Journal of Sichuan University (Engineering Science Edition)》 2005-01
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Feature Extraction for B-scan Fatty Liver Image

LIN Jiang-li~(1),WANG Xiao-yi~(1),LI De-yu~(2),WANG Tian-fu~(1),ZHENG Chang-qiong~(1),CHENG Yin-rong~(3)(1.Dept. of Biomedical Eng., Sichuan Univ., Chengdu 610065, China;2.Central. of Biomedical Eng., Sichuan Univ., Chengdu 610065, China;3.Ultrasonic office of Chengdu First People's Hospital,Chengdu 610016, China)  
During the feature extraction, three models were employed,which include mean intensity ratio of the near field and the far field, the gray level co-occurrence matrices(GLCMs), and the gray level run-length (GLRL). 10 statistics were extracted from the three models for each image. After the feature selection which involves hypothesis tests and artificial neural networks, there are only 4 features left for further researches including the Angular Second Moment (ASM), Entropy (ENT)and Inverse Differential Moment (IDM) from the GLCMs, as well as the Mean Intensity Ratio (MIR). Thus, the best feature vectors which indicate two classes of images are created with the four features. The feature vectors created with ASM, ENT, IDM and MIR have the best performance during the recognizing task.
【Fund】: 四川省应用基础研究项目(03JY029 072 2)
【CateGory Index】: R575.5
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