  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.

Medical Physics