Lo Chung-Ming, Moon Woo Kyung, Huang Chiun-Sheng, Chen Jeon-Hor, Yang Min-Chun, Chang Ruey-Feng
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Department of Radiology, Seoul National University Hospital, Seoul, Korea.
Ultrasound Med Biol. 2015 Jul;41(7):2039-48. doi: 10.1016/j.ultrasmedbio.2015.03.003. Epub 2015 Apr 2.
Radiologists likely incorrectly classify benign masses as Breast Imaging Reporting and Data System (BI-RADS) category 3. A computer-aided diagnosis (CAD) system was developed in this study as a second viewer to avoid misclassification of carcinomas. Sixty-nine biopsy-proven BI-RADS category 3 masses, including 21 malignant and 48 benign masses, were used to evaluate the CAD system. To improve the texture features, gray-scale variations between images were reduced by transforming pixels into intensity-invariant ranklet coefficients. The textures of the tumor and speckle pixels were extracted from the transformed ranklet images to provide more robust features than in conventional CAD systems. As a result, tumor texture and speckle texture with ranklet transformation achieved significantly better areas under the receiver operating characteristic curve (Az) compared with those without ranklet transformation (Az = 0.83 vs. 0.58 and Az = 0.80 vs. 0.56, p value < 0.05). The improved CAD system can be a second reader to confirm the classification of BI-RADS category 3 masses.
放射科医生可能会将良性肿块错误地归类为乳腺影像报告和数据系统(BI-RADS)3类。本研究开发了一种计算机辅助诊断(CAD)系统作为第二阅片者,以避免癌的误分类。69个经活检证实的BI-RADS 3类肿块,包括21个恶性肿块和48个良性肿块,用于评估CAD系统。为了改善纹理特征,通过将像素转换为强度不变的秩次系数来减少图像之间的灰度变化。从变换后的秩次图像中提取肿瘤和斑点像素的纹理,以提供比传统CAD系统更稳健的特征。结果,与未进行秩次变换的情况相比,进行秩次变换的肿瘤纹理和斑点纹理在受试者操作特征曲线(Az)下的面积显著更好(Az = 0.83对0.58以及Az = 0.80对0.56,p值<0.05)。改进后的CAD系统可以作为第二阅片者来确认BI-RADS 3类肿块的分类。