Allison J W, Barr L L, Massoth R J, Berg G P, Krasner B H, Garra B S
Department of Radiology, Arkansas Children's Hospital, Little Rock.
Radiographics. 1994 Sep;14(5):1099-108. doi: 10.1148/radiographics.14.5.7991816.
Because the human vision system cannot distinguish the broad range of gray values that a computer visual system can, computerized image analysis may be used to obtain quantitative information from ultrasonographic (US) real-time B-mode scans. Most quantitative US involves programming an off-line computer to accept, analyze, and display US image data in a way that enhances the detection of changes in small-scale structures and blood flow that occur with disease. Common image textural features used in quantitative US tissue characterization consist of first-order gray-level statistics (eg, occurrence frequency of gray levels independent of location or spatial relationship) and second-order gray-level statistics dependent on location and spatial relationship, including statistical analysis of gradient distribution, co-occurrence matrix, covariance matrix, run-length histogram, and fractal features. A customized tissue signature software has been developed to analyze image data obtained from clinical US scanners. Means comparison testing and multivariate analysis techniques are used to compare the numbers generated for a particular region of interest. By integrating these techniques into the radiologist's interpretation of the sonogram, the quantitative information gained may lead to earlier detection of lesions difficult to see with the human eye.
由于人类视觉系统无法区分计算机视觉系统所能识别的广泛灰度值，因此可利用计算机图像分析从超声（US）实时B模式扫描中获取定量信息。大多数定量超声检查需要对离线计算机进行编程，使其能够以增强对疾病相关的小尺度结构和血流变化检测的方式，接受、分析并显示超声图像数据。定量超声组织表征中常用的图像纹理特征包括一阶灰度统计量（例如，与位置或空间关系无关的灰度出现频率）以及依赖于位置和空间关系的二阶灰度统计量，其中包括梯度分布、共生矩阵、协方差矩阵、游程长度直方图和分形特征的统计分析。已开发出一种定制的组织特征软件，用于分析从临床超声扫描仪获取的图像数据。均值比较测试和多变量分析技术用于比较特定感兴趣区域生成的数值。通过将这些技术整合到放射科医生对超声图的解读中，所获得的定量信息可能有助于更早地检测出肉眼难以发现的病变。
