Lee Min-Zhao, Cai Weidong, Song Yang, Selvadurai Hiran, Feng David Dagan
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:3965-8. doi: 10.1109/EMBC.2013.6610413.
We present a prototype of a fully automated scoring system for chest radiographs (CXRs) in cystic fibrosis. The system was used to analyze real, clinical CXR data, to estimate the Shwachman-Kulczycki score for the image. Images were resampled and normalized to a standard size and intensity level, then segmented with a patch-based nearest-neighbor mapping algorithm. Texture features were calculated regionally and globally, using Tamura features, local binary patterns (LBP), gray-level co-occurrence matrix and Gabor filtering. Feature selection was guided by current understanding of the disease process, in particular the reorganization and thickening of airways. Combinations of these features were used as inputs for support vector machine (SVM) learning to classify each CXR, and evaluated using two-fold cross-validation for agreement with clinician scoring. The final computed score for each image was compared with the score assigned by a physician. Using this prototype system, we analyzed 139 CXRs from an Australian pediatric cystic fibrosis registry, for which texture directionality showed greatest discriminating power. Computed scores agreed with clinician scores in 75% of cases, and up to 90% of cases in discriminating severe disease from mild disease, similar to the level of human interobserver agreement for this dataset.
我们展示了一种用于囊性纤维化胸部X光片（CXR）的全自动评分系统的原型。该系统用于分析真实的临床CXR数据，以估计图像的Shwachman-Kulczycki评分。图像被重新采样并归一化到标准大小和强度水平，然后使用基于补丁的最近邻映射算法进行分割。使用田村特征、局部二值模式（LBP）、灰度共生矩阵和Gabor滤波在局部和全局计算纹理特征。特征选择以当前对疾病过程的理解为指导，特别是气道的重组和增厚。这些特征的组合被用作支持向量机（SVM）学习的输入，以对每个CXR进行分类，并使用二倍交叉验证进行评估，以与临床医生评分进行一致性比较。将每个图像的最终计算得分与医生给出的得分进行比较。使用这个原型系统，我们分析了来自澳大利亚儿科囊性纤维化登记处的139张CXR，其中纹理方向性显示出最大的鉴别力。在75%的病例中，计算得分与临床医生得分一致，在区分严重疾病和轻度疾病方面，高达90%的病例一致，与该数据集的人类观察者间一致性水平相似。
