Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images
Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images
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DOI：
10.1109/42.511750
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发表时间：
1996-08-01
影响因子：
10.6
通讯作者：
Youssef, ABM
中科院分区：
文献类型：
--
作者：
Kadah, YM;Farag, AA;Youssef, ABM
从超声图像诊断弥漫性肝病的视觉标准可以通过计算机组织分类来辅助。本文提出了一种从肝脏图像中提取组织定征参数的特征提取算法。得到的参数集被进一步处理，以获得最少数量的参数，这些参数代表用于分类的最有鉴别力的模式空间。该预处理步骤已应用于120多个不同的病理调查病例，以获得用于分类的学习数据，提取的特征被分为独立的训练集和测试集，并用于开发和比较两种统计。这些分类器的最佳标准被设置为具有最小的分类误差、易于实现和学习以及未来修改的灵活性。各种基于统计的分类算法。和神经网络的方法，并进行了测试，我们表明，非常好的诊断率，可以得到使用非传统的分类器训练的实际病人的数据。
Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in this paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification, The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical. and neural classifiers, The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical. and neural network methods are presented and tested, We show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data.