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 anal ysis may be used to obtain quantitative information from ultrasonograp hic (US) real-time B-mode scans. Most quantitative US involves program ming an off-line computer to accept, analyze, and display US image dat a in a way that enhances the detection of changes in small-scale struc tures and blood flow that occur with disease. Common image textural fe atures used in quantitative US tissue characterization consist of firs t-order gray-level statistics (eg, occurrence frequency of gray levels independent of location or spatial relationship) and second-order gra y-level statistics dependent on location and spatial relationship, inc luding statistical analysis of gradient distribution, co-occurrence ma trix, covariance matrix, run-length histogram, and fractal features. A customized tissue signature software has been developed to analyze im age data obtained from clinical US scanners. Means comparison testing and multivariate analysis techniques are used to compare the numbers g enerated for a particular region of interest. By integrating these tec hniques into the radiologist's interpretation of the sonogram, the qua ntitative information gained may lead to earlier detection of lesions difficult to see with the human eye.