Texture analysis to characterize strain distribution in elastograms

Abstract

Elastography is an upcoming and promising imaging modality which is used to map as an image, termed elastogram, information related to tissue elastic properties. An elastogram reflects strain information resulted from an applied stress. This paper presents a study of various image texture features as applied to simulated elastograms for the purpose of obtaining effective texture features. These features are those that exhibit non-overlapping distributions for different modulus distributions. Among various texture features studied, five are found to be effective for simulated elastograms. These include fractal signature, wavelet energy, and entropy, contrast and energy extracted from the sum and difference histograms. Upon the availability of clinical data, the results of this study will be used to classify abnormalities into malignant or benign tissue classes.