Horng Ming-Huwi
Department of Information Technology, National PingTung Institute of Commerce, No. 51 Min Sheng E. Road, Pingtung 900, Taiwan, ROC.
Comput Med Imaging Graph. 2007 Oct;31(7):485-91. doi: 10.1016/j.compmedimag.2007.05.001. Epub 2007 Jun 29.
A quantitative ultrasonic image evaluation system that generates a numerical severity measurement to assess the progression of chronic liver disease and assist clinical diagnosis is proposed in this paper. The progression of chronic liver disease is closely related to the amount of fibrosis of the liver parenchyma under microscopic examination. The powerful index, computer morphometry (CM) score developed in Sun et al. [Sun YN, Horng MH, Lin XZ. Automatic computer morphometry system techniques and applications in medical diagnosis. In: Cornelius TL, editor. Computational methods in biophysics, biomaterials, biotechnology and medical systems. Algorithm development, mathematical analysis and diagnostics, vol. 4. Boston/Dordrecht/London: Kluwer Academic Publishers; 2003. p. 33-50], accurately measures the fibrosis ratio of liver parenchyma from a microscopy of human liver specimens. Therefore, the results of the CM score of patients serves as an assessment basis for developing the disease measurement of the B-mode liver sonogram under echo-texture feature analysis methods. The radial basis function (RBF) network is used to establish the correlates between texture features of ultrasonic liver image and the corresponding CM score. The output of the RBF network is called the ultrasonic disease severity (UDS) score. The correct classification rate of 120 test images by using the UDS score is 92.5%. These promising results reveal that the UDS is capable of providing an important reference to diagnose chronic liver disease.
本文提出了一种定量超声图像评估系统，该系统可生成数值严重程度测量值，以评估慢性肝病的进展并辅助临床诊断。慢性肝病的进展与显微镜检查下肝实质的纤维化量密切相关。Sun等人开发的强大指标——计算机形态测量（CM）评分[Sun YN, Horng MH, Lin XZ. Automatic computer morphometry system techniques and applications in medical diagnosis. In: Cornelius TL, editor. Computational methods in biophysics, biomaterials, biotechnology and medical systems. Algorithm development, mathematical analysis and diagnostics, vol. 4. Boston/Dordrecht/London: Kluwer Academic Publishers; 2003. p. 33 - 50]，能从人类肝脏标本的显微镜图像中准确测量肝实质的纤维化比率。因此，患者的CM评分结果可作为在回声纹理特征分析方法下制定B型肝脏超声图疾病测量的评估依据。径向基函数（RBF）网络用于建立肝脏超声图像纹理特征与相应CM评分之间的相关性。RBF网络的输出称为超声疾病严重程度（UDS）评分。使用UDS评分对120幅测试图像的正确分类率为92.5%。这些令人鼓舞的结果表明，UDS能够为诊断慢性肝病提供重要参考。
