An ultrasonic image evaluation system for assessing the severity of chronic liver disease

被引:27
作者
Horng, Ming-Huwi [1 ]
机构
[1] Natl PingTung Inst Commerce, Dept Informat Technol, Pingtung 900, Peoples R China
关键词
ultrasonic liver image; ultrasonic scoring system; ultrasonic disease severity score; computer morphometry score; radial basis function network;
D O I
10.1016/j.compmedimag.2007.05.001
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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 parenchyrna 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. (c) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码:485 / 491
页数:7
相关论文
共 20 条
  • [1] Random field models in the textural analysis of ultrasonic images of the liver
    Bleck, JS
    Ranft, U
    Gebel, M
    Hecker, H
    WesthoffBleck, M
    Thiesemann, C
    Wagner, S
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 1996, 15 (06) : 796 - 801
  • [2] The use of the area under the roc curve in the evaluation of machine learning algorithms
    Bradley, AP
    [J]. PATTERN RECOGNITION, 1997, 30 (07) : 1145 - 1159
  • [3] APPLICATION OF NEURAL NETWORKS FOR THE CLASSIFICATION OF DIFFUSE LIVER-DISEASE BY QUANTITATIVE ECHOGRAPHY
    GEBBINCK, MSK
    VERHOEVEN, JTM
    THIJSSEN, JM
    SCHOUTEN, TE
    [J]. ULTRASONIC IMAGING, 1993, 15 (03) : 205 - 217
  • [4] TEXTURAL FEATURES FOR IMAGE CLASSIFICATION
    HARALICK, RM
    SHANMUGAM, K
    DINSTEIN, I
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1973, SMC3 (06): : 610 - 621
  • [5] Texture feature coding method for classification of liver sonography
    Horng, MH
    Sun, YN
    Lin, XZ
    [J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2002, 26 (01) : 33 - 42
  • [6] Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images
    Kadah, YM
    Farag, AA
    Zurada, JM
    Badawi, AM
    Youssef, ABM
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 1996, 15 (04) : 466 - 478
  • [7] FORMULATION AND APPLICATION OF A NUMERICAL SCORING SYSTEM FOR ASSESSING HISTOLOGICAL ACTIVITY IN ASYMPTOMATIC CHRONIC ACTIVE HEPATITIS
    KNODELL, RG
    ISHAK, KG
    BLACK, WC
    CHEN, TS
    CRAIG, R
    KAPLOWITZ, N
    KIERNAN, TW
    WOLLMAN, J
    [J]. HEPATOLOGY, 1981, 1 (05) : 431 - 435
  • [8] Li B., 1984, BIOMETRICS, V40, P358, DOI DOI 10.2307/2530946
  • [9] Computer morphometry for quantitative measurement of liver fibrosis: Comparison with Knodell's score, colorimetry and conventional description reports
    Lin, XZ
    Horng, MH
    Sun, YN
    Shiesh, SC
    Chow, NH
    Guo, XZ
    [J]. JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY, 1998, 13 (01) : 75 - 80
  • [10] Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform
    Mojsilovic, A
    Popovic, M
    Markovic, S
    Krstic, M
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 1998, 17 (04) : 541 - 549