Multiscale edge detection and parametric shape modeling for boundary delineation in optoacoustic images

TL;DR

This paper introduces a novel multiscale edge detection and ellipse fitting method for accurate boundary segmentation in optoacoustic small animal imaging, reducing manual intervention and improving delineation precision.

Contribution

The proposed approach combines multiscale edge detection with parametric ellipse fitting, eliminating the need for manual scale selection and seed point determination in optoacoustic image segmentation.

Findings

High accuracy in tissue boundary delineation

Minimal human intervention required

Effective removal of spurious edges

Abstract

In this article, we present a novel scheme for segmenting the image boundary (with the background) in optoacoustic small animal in vivo imaging systems. The method utilizes a multiscale edge detection algorithm to generate a binary edge map. A scale dependent morphological operation is employed to clean spurious edges. Thereafter, an ellipse is fitted to the edge map through constrained parametric transformations and iterative goodness of fit calculations. The method delimits the tissue edges through the curve fitting model, which has shown high levels of accuracy. Thus, this method enables segmentation of optoacoutic images with minimal human intervention, by eliminating need of scale selection for multiscale processing and seed point determination for contour mapping.