Quantification of Morphological Features in Non-Contrast Ultrasound Microvasculature Imaging

TL;DR

This paper develops methods to accurately quantify morphological features of microvasculature in non-contrast ultrasound images, addressing artifacts and segmentation challenges to improve potential disease biomarkers.

Contribution

It introduces novel filtering and segmentation techniques to enhance morphological analysis of microvasculature in ultrasound images with artifacts.

Findings

Improved accuracy in vessel diameter and tortuosity measurement.

Effective artifact reduction in microvasculature images.

Potential for new disease biomarkers from non-contrast ultrasound data.

Abstract

Morphological features of small vessels provide invaluable information regarding underlying tissue, especially in cancerous tumors. This paper introduces methods for obtaining quantitative morphological features from microvasculature images obtained by non-contrast ultrasound imaging. Those images suffer from the artifact that limit quantitative analysis of the vessel morphological features. In this paper we introduce processing steps to increase accuracy of the morphological assessment for quantitative vessel analysis in presence of these artifact. Specifically, artificats are reduced by additional filtering and vessel segments obtained by skeletonization of the regularized microvasculature images are further analyzed to satisfy additional constraints, such as diameter, and length of the vessel segments. Measurement of some morphological metrics, such as tortuosity, depends on preserving large vessel trunks that may be broken down into multiple branches. We propose two methods to address this problem. In the first method, small vessel segments are suppressed in the vessel filtering process via adjusting the size scale of the regularization. Hence, tortuosity of the large trunks can be more accurately estimated by preserving longer vessel segments. In the second approach, small connected vessel segments are removed by a combination of morphological erosion and dilation operations on the segmented vasculature images. These methods are tested on representative in vivo images of breast lesion microvasculature, and the outcomes are discussed. This paper provides a tool for quantification of microvasculature image from non-contrast ultrasound imaging may result in potential biomarkers for diagnosis of some diseases.