Amin Manar N, Rushdi Muhammad A, Marzaban Raghda N, Yosry Ayman, Kim Kang, Mahmoud Ahmed M
Department of Biomedical Engineering and Systems, Cairo University, Giza 12613, Egypt.
Endemic Medicine Department and Liver Unit, Faculty of Medicine, Cairo University, Giza 11652, Egypt.
Biomed Signal Process Control. 2019 Jul;52:84-96. doi: 10.1016/j.bspc.2019.03.010. Epub 2019 Apr 5.
Hepatic steatosis occurs when lipids accumulate in the liver leading to steatohepatitis, which can evolve into cirrhosis and consequently may end with hepatocellular carcinoma. Several automatic classification algorithms have been proposed to detect liver diseases. However, some algorithms are manufacturer-dependent, while others require extensive calculations and consequently prolonged computational time. This may limit the development of real-time and manufacturer-independent computer-aided detection of liver steatosis. This work demonstrates the feasibility of a computationally-efficient and manufacturer-independent wavelet-based computer-aided liver steatosis detection system using conventional B-mode ultrasound (US) imaging. Seven features were extracted from the approximation part of the second-level wavelet packet transform (WPT) of US images. The proposed technique was tested on two datasets of mice livers with and without gelatin embedding, in addition to a third dataset of human livers acquired using two different US machines. Using the gelatin-embedded mice liver dataset, the technique exhibited 98.8% accuracy, 97.8% sensitivity, and 100% specificity, and the frame classification time was reduced from 0.4814 s using original US images to 0.1444 s after WPT preprocessing. When the other mice liver dataset was used, the technique showed 85.74% accuracy, 84.4% sensitivity, and 88.5% specificity, and the frame classification time was reduced from 0.5612s to 0.2903 s. Using human liver image data, the best classifier exhibited 92.5% accuracy, 93.0% sensitivity, 91.0% specificity, and the classification time was reduced from 0.660 s to 0.146 s. This technique can be useful for developing computationally-efficient and manufacturer-independent noninvasive CAD systems for fatty liver detection.
当脂质在肝脏中积累导致脂肪性肝炎时，就会发生肝脂肪变性，脂肪性肝炎可发展为肝硬化，并最终可能演变为肝细胞癌。已经提出了几种自动分类算法来检测肝脏疾病。然而，一些算法依赖于制造商，而另一些算法需要大量计算，因此计算时间较长。这可能会限制肝脂肪变性的实时且独立于制造商的计算机辅助检测的发展。这项工作展示了一种基于小波的计算高效且独立于制造商的计算机辅助肝脂肪变性检测系统的可行性，该系统使用传统的B型超声（US）成像。从超声图像的二级小波包变换（WPT）的近似部分提取了七个特征。除了使用两台不同超声机器采集的人类肝脏的第三个数据集外，该技术还在有和没有明胶包埋的小鼠肝脏的两个数据集上进行了测试。使用明胶包埋的小鼠肝脏数据集，该技术的准确率为98.8%，灵敏度为97.8%，特异性为100%，帧分类时间从使用原始超声图像时的0.4814秒减少到WPT预处理后的0.1444秒。当使用另一个小鼠肝脏数据集时，该技术的准确率为85.74%，灵敏度为84.4%，特异性为88.5%，帧分类时间从0.5612秒减少到0.2903秒。使用人类肝脏图像数据，最佳分类器的准确率为92.5%，灵敏度为93.0%，特异性为91.0%，分类时间从0.660秒减少到0.146秒。这项技术对于开发用于脂肪肝检测的计算高效且独立于制造商的非侵入性计算机辅助诊断（CAD）系统可能是有用的。
