A Labeled Array Distance Metric for Measuring Image Segmentation Quality

TL;DR

This paper introduces two new distance metrics, LAD and MADLAD, for comparing labeled array outputs of image segmentation algorithms, enabling efficient evaluation of segmentation accuracy against ground truth.

Contribution

The paper proposes two novel, efficient distance metrics for comparing labeled images, improving the evaluation of segmentation algorithms with different labeling schemes.

Findings

LAD and MADLAD operate with O(N) complexity for images with N pixels.

The metrics effectively distinguish between manual and algorithm-generated labels.

They facilitate automated selection of optimal segmentation algorithms.

Abstract

This work introduces two new distance metrics for comparing labeled arrays, which are common outputs of image segmentation algorithms. Each pixel in an image is assigned a label, with binary segmentation providing only two labels ('foreground' and 'background'). These can be represented by a simple binary matrix and compared using pixel differences. However, many segmentation algorithms output multiple regions in a labeled array. We propose two distance metrics, named LAD and MADLAD, that calculate the distance between two labeled images. By doing so, the accuracy of different image segmentation algorithms can be evaluated by measuring their outputs against a 'ground truth' labeling. Both proposed metrics, operating with a complexity of $O(N)$ for images with $N$ pixels, are designed to quickly identify similar labeled arrays, even when different labeling methods are used. Comparisons are made between images labeled manually and those labeled by segmentation algorithms. This evaluation is crucial when searching through a space of segmentation algorithms and their hyperparameters via a genetic algorithm to identify the optimal solution for automated segmentation, which is the goal in our lab, SEE-Insight. By measuring the distance from the ground truth, these metrics help determine which algorithm provides the most accurate segmentation.