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《Computer Engineering and Applications》 2010-36
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Classification method of ultrasonic images of fatty livers based on threshold segmentation

LIU Jin-zhu1,WANG Jiang-he2,HONG Hui-wen2,LIU Yan-ling2,MIN Le-quan1,3 1.School of Information Engineering,University of Science and Technology Beijing,Beijing 100083,China2.Hepatology Department,Xiyuan Hospital of China Academy of Chinese Medical Sciences,Beijing 100091,China3.School of Applied Science,University of Science and Technology Beijing,Beijing 100083,China  
A classification method of ultrasonic liver image is studied based on threshold segmentation.Under the condition of minimizing the false-negative probabilityt,he confidence intervals of gray levels which are normal distributions in normal and fatty liver images can be obtained by minimizing the false-positive rate.Then classify the images by comparing the seg-mented images with the interval thresholds.The experimental results show that the classification rate of normal livers reaches to 100% for testing or training datat,he classification rate of fatty livers reaches to 96.3% for testing or training data.Com-pared with the Back Propagation Neural Network(BP NN) methodt,he method costs less time than BP NN method when they have the same performance.
【Fund】: 国家自然科学基金(No.60674059)~~
【CateGory Index】: TP391.41
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