Biomed. Eng. Program, Minnesota Univ., Minneapolis, MN.
IEEE Trans Med Imaging. 1996;15(4):466-78. doi: 10.1109/42.511750.
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. The authors show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data.
从超声图像诊断弥漫性肝脏疾病的视觉标准可以通过计算机组织分类来辅助。本文提出了特征提取算法，从肝脏图像中提取组织特征参数。所得参数集进一步处理，以获得表示分类最具判别模式空间的最小参数数。该预处理步骤已应用于 120 多个不同的病理研究病例，以获得分类的学习数据。提取的特征分为独立的训练集和测试集，并用于开发和比较统计和神经网络分类器。为这些分类器设置了最佳标准，以实现最小的分类错误、易于实现和学习，以及为未来修改提供灵活性。本文提出并测试了基于统计和神经网络方法的各种分类算法。作者表明，使用基于实际患者数据训练的非传统分类器可以获得非常好的诊断率。
