2D Short-Time Fourier Transform for local morphological analysis of meibomian gland images

Abstract

Meibography is becoming an integral part of dry eye diagnosis. Being objective and repeatable this imaging technique is used to guide treatment decisions and determine the disease status. Especially desirable is the possibility of automatic (or semi-automatic) analysis of a meibomian image for quantification of a particular gland’s feature. Recent reports suggest that in addition to the measure of gland atrophy (quantified by the well-established “drop-out area” parameter), the gland’s morphological changes may carry equally clinically useful information. Here we demonstrate the novel image analysis method providing detailed information on local deformation of meibomian gland pattern. The developed approach extracts from every Meibomian image a set of six morphometric color-coded maps, each visualizing spatial behavior of different morphometric parameter. A more detailed analysis of those maps was used to perform automatic classification of Meibomian glands images. The method for isolating individual morphometric components from the original meibomian image can be helpful in the diagnostic process. It may help clinicians to see in which part of the eyelid the disturbance is taking place and also to quantify it with a numerical value providing essential insight into Meibomian gland dysfunction pathophysiology.

An effective fingerprint orientation field estimation method using differential values of grayscale intensity

“…In recent decades, different OF estimation methods have been proposed, including gradient-based methods ( Awad, 2016 ; Li et al, 2018 ), slit-based approaches ( Oliveira & Leite, 2008 ), frequency domain-based estimations ( Ciezar & Pochylski, 2022 ; Park & Park, 2005 ), learning-based models ( Cao & Jain, 2018 ; Qu et al, 2018 ), and gray-level variance methods ( Dyre & Sumathi, 2017 ; Bian et al, 2019 ; Turroni et al, 2011 ). Currently, the most popular of these fingerprint image OF estimation approaches is the gradient-based method because of its high resolution, high accuracy, and low computational demand ( Turroni et al, 2011 ; Sharma & Dey, 2019 ; Ratha, Chen & Jain, 1995 ; Gottschlich, Mihailescu & Munk, 2009 ; Bazen & Gerez, 2002 ; Liu & Dai, 2006 ; Kekre & Bharadi, 2009 ; Mei, Sun & Xia, 2009 ; Wieclaw, 2013 ; Wang, Hu & Han, 2007 ; Bazen & Gerez, 2000 ).…”

A Deep Learning Approach for Meibomian Gland Appearance Evaluation

“…The aforementioned studies have proposed various approaches to quantify visible Meibomian gland structure, which have successfully replaced other conventional approaches such as thresholding, edge detection, region growing, and clustering. 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 These approaches rely on predefined rules and heuristics to identify and segment different regions of an image. In contrast, deep learning methods use neural networks to learn representations of features directly from the image data, without the need for manual feature engineering.…”