In-vivo human liver tissue differentiation

Proceedings of the 1988 Fourteenth Annual Northeast Bioengineering Conference Pub Date : 1988-03-10 DOI:10.1109/NEBC.1988.19346

N. Botros

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Abstract

A pattern-recognition algorithm and the required instrumentation to apply it for in vivo human liver tissue differentiation are discussed. The algorithm has been tested successfully, with a confidence interval of 68.27%, on 25 subjects with no history of liver diseases and 15 subjects with different types of abnormalities. Differentiation between normal and abnormal liver tissue is accomplished by calculating the Euclidean distance between a reference vector and a pattern vector. The elements of the reference vector are the precalculated values of the average attenuation and backscattering coefficients of normal liver tissue at each frequency interval in the range from 1.5 to 4.5 MHz. Elements of the pattern vector are the average values of the two coefficients for the liver tissue under consideration at each frequency interval. This distance is a measure of the probability that the liver under consideration is normal. An empirical threshold is selected such that if the distance is less than the threshold, then the liver is declared normal otherwise it is abnormal. The instrumentation implemented is a high speed microprocessor-based data acquisition and analysis system. The system digitizes the backscattered ultrasound signal and stores the digitized data in a microcomputer where it is analyzed.<>

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活体人肝组织分化

本文讨论了一种模式识别算法和将其应用于体内人肝组织分化所需的仪器。该算法在25名无肝脏病史的受试者和15名不同类型异常的受试者上测试成功，置信区间为68.27%。通过计算参考向量和模式向量之间的欧氏距离来实现正常和异常肝组织的区分。参考向量的元素是预先计算的正常肝组织在1.5 ~ 4.5 MHz范围内每个频率间隔的平均衰减系数和后向散射系数的值。模式向量的元素是在每个频率区间内考虑的肝组织的两个系数的平均值。这个距离是衡量肝脏正常的概率。选择一个经验阈值，如果距离小于阈值，则肝脏被宣布为正常，否则它是异常的。所实现的仪器是一个基于高速微处理器的数据采集与分析系统。该系统对后向散射超声信号进行数字化处理，并将数字化后的数据存储在微机中进行分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 1988 Fourteenth Annual Northeast Bioengineering Conference

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