Ribeiro Ricardo, Tato Marinho Rui, Sanches J Miguel
Institute for Systems and Robotics, IST, Lisbon, Portugal.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6547-50. doi: 10.1109/EMBC.2012.6347494.
Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. Steatosis is usually a diffuse liver disease, since it is globally affected. However, steatosis can also be focal affecting only some foci difficult to discriminate. In both cases, steatosis is detected by laboratorial analysis and visual inspection of ultrasound images of the hepatic parenchyma. Liver biopsy is the most accurate diagnostic method but its invasive nature suggest the use of other non-invasive methods, while visual inspection of the ultrasound images is subjective and prone to error. In this paper a new Computer Aided Diagnosis (CAD) system for steatosis classification and analysis is presented, where the Bayes Factor, obatined from objective intensity and textural features extracted from US images of the liver, is computed in a local or global basis. The main goal is to provide the physician with an application to make it faster and accurate the diagnosis and quantification of steatosis, namely in a screening approach. The results showed an overall accuracy of 93.54% with a sensibility of 95.83% and 85.71% for normal and steatosis class, respectively. The proposed CAD system seemed suitable as a graphical display for steatosis classification and comparison with some of the most recent works in the literature is also presented.
肝脂肪变性是一种常见疾病，通常与社会和遗传因素有关。早期检测和量化很重要，因为它可能会发展为肝硬化。脂肪变性通常是一种弥漫性肝病，因为整个肝脏都会受到影响。然而，脂肪变性也可能是局灶性的，仅影响一些难以区分的病灶。在这两种情况下，脂肪变性都是通过实验室分析和对肝实质超声图像的目视检查来检测的。肝活检是最准确的诊断方法，但其侵入性表明需要使用其他非侵入性方法，而超声图像的目视检查具有主观性且容易出错。本文提出了一种用于脂肪变性分类和分析的新型计算机辅助诊断（CAD）系统，该系统基于从肝脏超声图像中提取的客观强度和纹理特征计算局部或全局的贝叶斯因子。主要目标是为医生提供一种应用程序，以便更快、准确地进行脂肪变性的诊断和量化，即在筛查方法中。结果显示总体准确率为93.54%，正常和脂肪变性类别的敏感性分别为95.83%和85.71%。所提出的CAD系统似乎适合作为脂肪变性分类的图形显示，并与文献中一些最新的研究进行了比较。
