Ultrasonic Tissue Characterization By Texture Analysis: Elimination Of Tissue-Independent Factors

Abstract

When image texture analysis methods are used for ultrasonic tissue characterization, the discrimination results obtained by statistical pattern discrimination methods must be interpreted carefully in order to avoid pseudo-discriminations due to differences in exa-mination procedures and system settings. This study examines the dependence of popular texture analysis methods on transducer-specific diffraction characteristics, B-mode image reconstruction and sampling factors, i.e. size and position of the selected Region-of-Interest. It is shown that image analysis should always be based on diffraction-corrected ultra-sound signals. In large-organ applications, e.g. liver, polar reconstruction yielded more stable results than cartesian reconstruction, especially when texture measures from the greylevel runlength matrices or power spectrum are used. Analyzing clinical and synthesized ultrasound images, we found that the first-order greylevel statistics: Mean greylevel, skewness and excess as well as the second-order sta-tistics: Correlation of greylevel cooccurrences proved to be stable with respect to tissue-independent factors as well as sufficiently sensitive to tissue differences.