Information Entropy-Based Framework for Quantifying Curve Tortuosity in Meibomian Glands Uneven Atrophy.

Purpose: In the field of medical image analysis, 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 curve tortuosity quantification and demonstrate its effectiveness through the evaluation of meibomian gland (MG) atrophy uniformity, serving as a representative application scenario. Methods: We introduce an information entropy-based framework for quantifying MGs boundary curve tortuosity that integrates probability modeling with entropy theory and incorporates domain transformation of curve data. First, we conducted numerical simulations to preliminarily assess the stability and validity of the method. Subsequently, the framework was applied to quantify the spatial uniformity of MG atrophy and to analyze the difference in this uniformity between Demodex-negative and Demodex-positive patient groups. Results: These 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. Conclusions: These findings highlight the clinical utility of the proposed framework in MG boundary curve tortuosity analysis and its potential as a generalizable tool for quantitative morphological evaluation in medical diagnostics. Translational Relevance: By leveraging readily available reference curves, this method directly bridges basic morphometric research and clinical diagnostics for ocular surface disease.