Third-Order Correlation for Ultrasound Image Classification

Abstract

We introduce a novel approach for distinguishing between benign and malignant tumors in breast ultrasound images using a set of features derived from third-order statistics. Unlike second-order statistics, which measure relationships between pairs of pixels, third-order statistics capture correlations among three pixels (triple correlation), providing a complete characterization of the information within an image. The third-order features were computed from the triple correlation distribution and include the mean, standard deviation, kurtosis, skewness, and entropy. Our findings reveal that third-order features significantly outperform commonly used second-order ones in key classification metrics, including accuracy, F1 score, specificity, and precision in random forest classification. These results suggest that metrics based on third-order statistics can uncover previously unrecognized patterns, offering valuable insights for improving tumor classification in ultrasound imaging.Clinical Relevance- This study demonstrates the enhanced classification capability of third-order features compared to second-order features for breast ultrasound tumors. We propose that the novel integration of these metrics can improve classification performance and can serve as valuable additions to the toolkit for breast ultrasound analysis, particularly for borderline or questionable tumor pathology. Furthermore, the versatility of this approach extends to other modalities including mammography and MRI.