Acharya U Rajendra, Faust Oliver, Sree S Vinitha, Alvin Ang Peng Chuan, Krishnamurthi Ganapathy, Seabra José C R, Sanches João, Suri Jasjit S
Department of Electrical and Computer Engineering, Ann Polytechnic, Singapore 599489.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4489-92. doi: 10.1109/IEMBS.2011.6091113.
Quantitative characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic™, patented technology from Biomedical Technologies, Inc., CA, USA) which analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Encouraged by this result, we feel that these features can be used to identify the plaque tissue type. Therefore, we propose an integrated index, a unique number called symptomatic asymptomatic carotid index (SACI) to discriminate symptomatic and asymptomatic carotid ultrasound images. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.
颈动脉粥样硬化的定量表征以及将其分类为有症状或无症状对于一系列心血管疾病的诊断和治疗规划至关重要。本文介绍了一种计算机辅助诊断（CAD）系统（Atheromatic™，美国加利福尼亚州生物医学技术公司的专利技术），该系统可分析超声图像并将其分类为有症状和无症状。分类结果基于离散小波变换、高阶谱和纹理特征的组合。在本研究中，我们比较了具有不同核的支持向量机（SVM）分类器。具有径向基函数（RBF）核的分类器实现了91.7%的准确率、97%的灵敏度和80%的特异性。受此结果鼓舞，我们认为这些特征可用于识别斑块组织类型。因此，我们提出了一个综合指标，一个名为有症状无症状颈动脉指数（SACI）的唯一数字，以区分有症状和无症状的颈动脉超声图像。我们希望这个SACI可以作为血管外科医生日常筛查的辅助工具。
