Classification of simulated elastograms based on texture features

Elastography is a new imaging modality which provides an image of tissue elastic properties. Simulated elastograms are used to study the ability of textural features to discriminate between abnormalities with different levels of hardness and density. Elastograms are simulated for different signal processing parameters as well as for a non-uniform stress field. 15 selected texture features are extracted. The Gaussian classifier does not perform well due to the complexity of clusters. Hence, a neural network classifier is trained and tested to discriminate between the classes. The results indicate that the texture features perform well under variations of the signal processing parameters and non-uniformity of the applied stress field. This simulation study provides a guiding mechanism to discriminate between malignant and benign tissue abnormalities once clinical data becomes available