Smutek Daniel, Sára Radim, Sucharda Petr, Tjahjadi Tardi, Svec Martin
3rd Department of Medicine, 1st Medical Faculty, Charles University, Prague, Czech Republic.
Ultrasound Med Biol. 2003 Nov;29(11):1531-43. doi: 10.1016/s0301-5629(03)01049-4.
The current practice in assessing sonographic findings of chronic inflamed thyroid tissue is mainly qualitative, based just on a physician's experience. This study shows that inflamed and healthy tissues can be differentiated by automatic texture analysis of B-mode sonographic images. Feature selection is the most important part of this procedure. We employed two selection schemes for finding recognition-optimal features: one based on compactness and separability and the other based on classification error. The full feature set included Muzzolini's spatial features and Haralick's co-occurrence features. These features were selected on a set of 2430 sonograms of 81 subjects, and the classifier performance was evaluated on a test set of 540 sonograms of 18 independent subjects. A classification success rate of 100% was achieved with as few as one optimal feature among the 129 texture characteristics tested. Both selection schemes agreed on the best features. The results were confirmed on the independent test set. The stability of the results with respect to sonograph setting, thyroid gland segmentation and scanning direction was tested.
目前评估慢性炎症性甲状腺组织超声检查结果的做法主要是定性的，仅基于医生的经验。本研究表明，通过对B型超声图像进行自动纹理分析，可以区分炎症组织和健康组织。特征选择是该过程中最重要的部分。我们采用了两种选择方案来寻找识别最优特征：一种基于紧致性和可分性，另一种基于分类误差。完整的特征集包括穆佐利尼的空间特征和哈勒克的共生特征。这些特征是从81名受试者的2430幅超声图中选取的，分类器性能在18名独立受试者的540幅超声图测试集上进行评估。在测试的129个纹理特征中，仅用一个最优特征就实现了100%的分类成功率。两种选择方案对最佳特征的意见一致。结果在独立测试集上得到了证实。测试了结果相对于超声仪设置、甲状腺分割和扫描方向的稳定性。
