Understanding the process of quantitative ultrasonic tissue characterization

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.