Objective: This study introduces a novel method for the automated detection and quantification of meibomian gland morphology using gray value distribution profiles. The approach addresses limitations in traditional manual and deep learning-based meibography analysis, which are often time-consuming and prone to variability. Methods: This study enrolled 100 volunteers (mean age 40 ± 16 years, range 18–85) who suffered from dry eye and responded to the Ocular Surface Disease Index questionnaire for scoring ocular discomfort symptoms and infrared meibography for capturing imaging of meibomian glands. By leveraging pixel brightness variations, the algorithm provides real-time detection and classification of long, medium, and short meibomian glands, offering a quantitative assessment of gland atrophy. Results: A novel parameter, namely “atrophy index”, a quantitative measure of gland degeneration, is introduced. Atrophy index is the first instrumental measurement to assess single- and multiple-gland morphology. Conclusions: This tool provides a robust, scalable metric for integrating quantitative meibography into clinical practice, making it suitable for real-time screening and advancing the management of dry eyes owing to meibomian gland dysfunction.