Information Entropy-Based Framework for Quantifying Tortuosity in Meibomian Gland Uneven Atrophy

TL;DR

This paper introduces an information entropy-based framework for quantifying curve tortuosity, specifically applied to assess meibomian gland atrophy uniformity, offering a robust and objective medical diagnostic tool.

Contribution

The study presents a novel entropy-based method for tortuosity quantification that compares target curves to reference curves, improving robustness over traditional measures.

Findings

Significant difference in tortuosity uniformity between patient groups

High diagnostic accuracy with AUC of 0.8768

Framework effectively distinguishes disease states

Abstract

In the medical image analysis field, precise quantification of curve tortuosity plays a critical role in the auxiliary diagnosis and pathological assessment of various diseases. In this study, we propose a novel framework for tortuosity quantification and demonstrate its effectiveness through the evaluation of meibomian gland atrophy uniformity,serving as a representative application scenario. We introduce an information entropy-based tortuosity quantification framework that integrates probability modeling with entropy theory and incorporates domain transformation of curve data. Unlike traditional methods such as curvature or arc-chord ratio, this approach evaluates the tortuosity of a target curve by comparing it to a designated reference curve. Consequently, it is more suitable for tortuosity assessment tasks in medical data where biologically plausible reference curves are available, providing a more robust and objective evaluation metric without relying on idealized straight-line comparisons. First, we conducted numerical simulation experiments to preliminarily assess the stability and validity of the method. Subsequently, the framework was applied to quantify the spatial uniformity of meibomian gland atrophy and to analyze the difference in this uniformity between \textit{Demodex}-negative and \textit{Demodex}-positive patient groups. The results demonstrated a significant difference in tortuosity-based uniformity between the two groups, with an area under the curve of 0.8768, sensitivity of 0.75, and specificity of 0.93. These findings highlight the clinical utility of the proposed framework in curve tortuosity analysis and its potential as a generalizable tool for quantitative morphological evaluation in medical diagnostics.