Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
Comput Biol Med. 2018 Apr 1;95:55-62. doi: 10.1016/j.compbiomed.2018.02.002. Epub 2018 Feb 7.
Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be helpful to cross-verify the severity of nodules. This paper proposes a new CAD system to characterize thyroid nodules using optimized multi-level elongated quinary patterns. In this study, higher order spectral (HOS) entropy features extracted from these patterns appropriately distinguished benign and malignant nodules under particle swarm optimization (PSO) and support vector machine (SVM) frameworks. Our CAD algorithm achieved a maximum accuracy of 97.71% and 97.01% in private and public datasets respectively. The evaluation of this CAD system on both private and public datasets confirmed its effectiveness as a secondary tool in assisting radiological findings.
超声成像是放射科医生用于识别甲状腺结节位置的最常用的可视化工具之一。然而，结节的视觉评估较为困难，并且经常受到观察者间和观察者内变异性的影响。因此，计算机辅助诊断（CAD）系统有助于对结节的严重程度进行交叉验证。本文提出了一种新的 CAD 系统，该系统使用优化的多级细长五进制模式对甲状腺结节进行特征描述。在这项研究中，从这些模式中提取的高阶谱（HOS）熵特征在粒子群优化（PSO）和支持向量机（SVM）框架下适当地区分了良性和恶性结节。我们的 CAD 算法在私有数据集和公共数据集上的最高准确率分别达到了 97.71%和 97.01%。该 CAD 系统在私有数据集和公共数据集上的评估证实了其作为辅助放射学发现的二级工具的有效性。
