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.
Gaussian modulated sinusoids are used in S-transform to extract time-local and space-local spectral information. Similar data sets recorded at neighboring spatial locations may be used with cross spectral analysis to determine frequency localized velocity spectrum. The 2D S-transform is used in image analysis for space localized wavenumber spectra. Local changes in the image spectrum are used to define textural boundaries on images. This paper summarizes several of the research projects involving S-transforms currently in progress at the University of Western Ontario including the application of the 2D S-transform to texture analysis, recognition, and the classification of images.