LAUSR

Optical-resolution photoacoustic microscopy has been validated as an ideal tool for angiographic studies. Quantitative vascular analysis reveals critical information where vessel segmentation plays the key step. The comm-only used Hessian filter method suffers from varying accuracy due to the multi-kernel strategy. In this work, we developed a Hessian filter-assisted, adaptive thresholding vessel segmentation algorithm. Its performance is validated by a digital phantom and in vivo images which demonstrates a superior and consistent accuracy of 0.987 regardless of kernel selection. Subtle vessel change detection is further tested in two longitudinal studies on blood pressure agents. In the antihypotensive case, the proposed method detected a twice larger vasoconstriction over the Hessian filter method. In the antihypertensive case, the proposed method detected a vasodilation of 21.2%, while the Hessian filter method failed in change detection. The proposed algorithm may further push the limit of quantitative imaging on angiographic applications.