关键词: 脂肪肝, 超声肝图像, 分类, 阈值分割

Abstract: A classification method of ultrasonic liver image is studied based on threshold segmentation.Under the condition of minimizing the false-negative probability，the 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 segmented images with the interval thresholds.The experimental results show that the classification rate of normal livers reaches to 100% for testing or training data，the classification rate of fatty livers reaches to 96.3% for testing or training data.Compared with the Back Propagation Neural Network（BP NN） method，the method costs less time than BP NN method when they have the same performance.

Key words: fatty liver, ultrasonic liver image, classification, threshold segmentation