A Locally Adapting Technique for Boundary Detection using Image Segmentation

TL;DR

This paper introduces a supervised, locally adaptive image segmentation method that uses spatial information and maximum likelihood estimation to accurately detect boundaries in images with overlapping intensity regions, providing uncertainty quantification.

Contribution

The paper presents a novel boundary detection technique that adaptively incorporates local spatial information and quantifies uncertainty, improving boundary localization in complex images.

Findings

Successfully applied to radiographs and optical images

Provides boundary locations with uncertainty bands

Outperforms traditional segmentation methods

Abstract

Rapid growth in the field of quantitative digital image analysis is paving the way for researchers to make precise measurements about objects in an image. To compute quantities from the image such as the density of compressed materials or the velocity of a shockwave, we must determine object boundaries. Images containing regions that each have a spatial trend in intensity are of particular interest. We present a supervised image segmentation method that incorporates spatial information to locate boundaries between regions with overlapping intensity histograms. The segmentation of a pixel is determined by comparing its intensity to distributions from local, nearby pixel intensities. Because of the statistical nature of the algorithm, we use maximum likelihood estimation theory to quantify uncertainty about each boundary. We demonstrate the success of this algorithm on a radiograph of a multicomponent cylinder and on an optical image of a laser-induced shockwave, and we provide final boundary locations with associated bands of uncertainty.