A Novel Distance-Based Metric for Quality Assessment in Image Segmentation

• URL: http://arxiv.org/abs/2504.00023v1
• Date: Fri, 28 Mar 2025 12:02:09 GMT
• Title: A Novel Distance-Based Metric for Quality Assessment in Image Segmentation
• Authors: Niklas Rottmayer, Claudia Redenbach,
• Abstract summary: We introduce the Surface Consistency Coefficient ( SCC), a novel distance-based quality metric.<n> SCC quantifies the spatial distribution of errors based on their proximity to the surface of the structure.<n>We demonstrate the robustness and effectiveness of SCC in distinguishing errors near the surface from those further away.
• Score: 0.7673339435080445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The assessment of segmentation quality plays a fundamental role in the development, optimization, and comparison of segmentation methods which are used in a wide range of applications. With few exceptions, quality assessment is performed using traditional metrics, which are based on counting the number of erroneous pixels but do not capture the spatial distribution of errors. Established distance-based metrics such as the average Hausdorff distance are difficult to interpret and compare for different methods and datasets. In this paper, we introduce the Surface Consistency Coefficient (SCC), a novel distance-based quality metric that quantifies the spatial distribution of errors based on their proximity to the surface of the structure. Through a rigorous analysis using synthetic data and real segmentation results, we demonstrate the robustness and effectiveness of SCC in distinguishing errors near the surface from those further away. At the same time, SCC is easy to interpret and comparable across different structural contexts.

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