Automated Detection of Grayscale Bar and Distance Scale in Ultrasound Images

摘要

Computer assisted diagnosis algorithms are evaluated by testing them against wide-ranging sets of images arising from real clinical conditions. Detection of the distance scale and the reference grayscale present in most ultrasound images can be used to automate the calibration of physical per-pixel distances and grayscale normalization over heterogeneously acquired ultrasound datasets. This work presents novel methods for automated detection of (ⅰ) the distance scale and the spacing between its gradations, (ⅱ) the reference grayscale. The distance scale was detected by searching for regular peaks in the 1-D autocorrelation of image pixel columns. The grayscale bar was detected by searching for contiguous sets of columns with long sequences of monotonically changing intensity. In tests on over 1000 images the distance scale detection rate was 94.8% and the correct gradation spacing was determined 91.2% of the time. The reference grayscale detection rate was 100%. A confidence measure was also introduced to characterize the certainty of the distance scale detection. An optimal confidence threshold for flagging low-confidence results that minimizes human intervention without risk of incorrect results remaining unflagged was established through ROC curve analysis.