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

2025-07-24

AI Summary

Quantifying curve tortuosity in medical images—e.g., assessing uniformity of meibomian gland atrophy—remains challenging, as conventional metrics (e.g., curvature, arc-to-chord ratio) rely on idealized straight-line references, compromising robustness and biological plausibility. To address this, we propose a novel information-theoretic framework for quantifying curve distortion: it models curve morphology probabilistically, employs domain transformation, and compares against empirically derived reference curves—thereby eliminating dependence on ideal geometric assumptions. The method demonstrates numerical stability via synthetic experiments and is validated on clinical meibomian gland images. Statistical analysis reveals significant differences in distortion uniformity between Demodex-negative and -positive groups (AUC = 0.8768, sensitivity = 0.75, specificity = 0.93), confirming its objectivity, robustness, and translational potential for ophthalmic diagnostics.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Adversarial Attacks & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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 extit{Demodex}-negative and extit{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.

Problem

• Quantify meibomian gland atrophy tortuosity using entropy
• Compare curve tortuosity against biological reference curves
• Assess clinical differences in Demodex-positive vs negative groups

Innovation

• Information entropy-based tortuosity quantification framework
• Probability modeling with entropy theory integration
• Domain transformation of curve data