Jurkonis R, Janušauskas A, Marozas V, Jegelevičius D, Daukantas S, Patašius M, Paunksnis A, Lukoševičius A
Biomedical Engineering Institute, Kaunas University of Technology, Studentu Street 65, 51369 Kaunas, Lithuania.
ScientificWorldJournal. 2012;2012:870869. doi: 10.1100/2012/870869. Epub 2012 May 2.
Algorithms and software were developed for analysis of B-scan ultrasonic signals acquired from commercial diagnostic ultrasound system. The algorithms process raw ultrasonic signals in backscattered spectrum domain, which is obtained using two time-frequency methods: short-time Fourier and Hilbert-Huang transformations. The signals from selected regions of eye tissues are characterized by parameters: B-scan envelope amplitude, approximated spectral slope, approximated spectral intercept, mean instantaneous frequency, mean instantaneous bandwidth, and parameters of Nakagami distribution characterizing Hilbert-Huang transformation output. The backscattered ultrasound signal parameters characterizing intraocular and orbit tissues were processed by decision tree data mining algorithm. The pilot trial proved that applied methods are able to correctly classify signals from corpus vitreum blood, extraocular muscle, and orbit tissues. In 26 cases of ocular tissues classification, one error occurred, when tissues were classified into classes of corpus vitreum blood, extraocular muscle, and orbit tissue. In this pilot classification parameters of spectral intercept and Nakagami parameter for instantaneous frequencies distribution of the 1st intrinsic mode function were found specific for corpus vitreum blood, orbit and extraocular muscle tissues. We conclude that ultrasound data should be further collected in clinical database to establish background for decision support system for ocular tissue noninvasive differentiation.
开发了用于分析从商业诊断超声系统获取的B扫描超声信号的算法和软件。这些算法在反向散射频谱域中处理原始超声信号，该频谱域是使用两种时频方法获得的：短时傅里叶变换和希尔伯特-黄变换。眼组织选定区域的信号由以下参数表征：B扫描包络幅度、近似频谱斜率、近似频谱截距、平均瞬时频率、平均瞬时带宽以及表征希尔伯特-黄变换输出的 Nakagami 分布参数。通过决策树数据挖掘算法处理表征眼内和眼眶组织的反向散射超声信号参数。初步试验证明，所应用的方法能够正确分类来自玻璃体血液、眼外肌和眼眶组织的信号。在26例眼组织分类中，当组织被分类为玻璃体血液、眼外肌和眼眶组织类别时出现了1次错误。在该初步分类中，发现光谱截距和第一本征模函数瞬时频率分布的 Nakagami 参数对于玻璃体血液、眼眶和眼外肌组织具有特异性。我们得出结论，应在临床数据库中进一步收集超声数据，以便为眼组织无创鉴别决策支持系统建立背景。
