2D fourier transform for global analysis and classification of meibomian gland images

Ciężar, Kamila; Pochylski, Mikołaj

doi:10.1016/j.jtos.2020.09.005

Cited by 14 publications

(12 citation statements)

References 25 publications

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“…This observation can be explained by broad and strongly overlapped probability distributions (marginal plots in Fig 7a) making the distinction between the classes inherently uncertain. Comparing current results from PCA classifier with the previous outcomes [33] one sees that increasing the number of descriptive features (current 30 features vs. previous 2) is not the way for improving categorization efficiency. The data reduction method based on maximizing variability in the data set (utilized by PCA approach) has probably hit the limit of efficiency.…”

Section: Plos Onementioning

confidence: 83%

“…Similarly to σ q map, regions with low values of σ θ correspond to gland pattern with well-defined orientation. We recall that this parameter but measured on the global scale (for the whole Meibomian image), was used previously as a measure of anisotropy in gland periodicity [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

“…The Meibomian gland image analysis developed in this work was tested on the images acquired in our recent research [ 33 ]. A total of 146 images (2 images for both upper eyelids of each patient) were collected using home-built meibographic imaging equipment (details in the in the S1 Appendix ).…”

Section: Methodsmentioning

confidence: 99%

“…There are several works introducing the meibomian gland classification based on their deformation, but so far the gold standard has not been established [9][10][11][12][24][25][26][27]. In searching for other objective descriptors of MG morphological condition, we have recently presented an approach for quantifying and classifying Meibomian images using 2D Fourier Transform (2DFT) [33]. This global analysis, performed on the whole set of the glands, demonstrated that information on mean gland frequency (connected with mean width of glands or inter-gland section) and anisotropy in gland periodicity (related to mean spread in gland directions) can be used for automatic image classification.…”

Section: Introductionmentioning

confidence: 99%

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

Ciężar

Pochylski

2022

PLoS ONE

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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.

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Section: Plos Onementioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ciężar

Pochylski

2022

PLoS ONE

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“…Also, MG vagueness was found significantly correlated with MGD severity at all levels, MG acinar shortest diameter (r = −0.278, p = 0.017), OSDI questionnaire score (r = −0.3271, p = 0.001) and tear break-up time (r = 0.405, p =< 0.001). Ciężar and Pochylski (2020) applied the Fourier image transformation to the MG analysis. In particular, the authors proposed two new metrics such as the "mean gland frequency" (i.e., number of glands per unit length) and "anisotropy of gland periodicity" to study the whole eyelid area.…”

Section: Infrared Lightmentioning

confidence: 99%

Imaging the tarsal plate: A Mini-Review

Recchioni

2021

Scand J Optom Vis Sci

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Imaging the tarsal plate and the meibomian glands (MG) grants new opportunities for ophthalmic practitioners who work in the field of the ocular surface and dry eye across the globe. The secretory role of MG plays a fundamental part in protecting the moisture in front of the eye surface by creating an active shield made of meibum (lipid) which prevents tear evaporation and causes dry eye. Evidence from the most popular Dry Eye Workshop reports (2007 and 2016) demonstrate that MG dysfunction is the first cause of evaporative dry eye which is also the most common cause of dry eye and ocular surface discomfort. Fortunately, during the last years, a plethora of new devices for MG observation, diagnosis and follow-up have been made available in the market. These devices range from invasive to minimally invasive, high to low-tech and from being expensive to low-cost. The objective of this mini-review is to condense the latest evidence in MG imaging by providing a narrative overview on the most common technologies plus some other newer aspects which might guide clinicians and researchers in the field of the ocular surface and dry eye.

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Technical note: On the development of an outcome‐driven frequency filter for improving radiomics‐based modeling of human papillomavirus (HPV) in patients with oropharyngeal squamous cell carcinoma

Bagher‐Ebadian¹,

Zhu²,

Siddiqui³

et al. 2021

Medical Physics

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Purpose To implement an outcome‐driven frequency filter for improving radiomics‐based modeling of human papillomavirus (HPV) for patients with oropharyngeal squamous cell carcinoma (OPSCC). Methods and materials One hundred twenty‐eight OPSCC patients with known HPV status (60‐HPV+ and 68‐HPV–, confirmed by immunohistochemistry‐P16 protein testing) were retrospectively studied. A 3D Discrete Fourier Transform was applied on contrast‐enhanced computed tomography (CE‐CT) images of patient gross tumor volumes (GTVs) to transform intensity distributions to the frequency domain and estimate frequency power spectrums of HPV– and HPV+ patient cohorts. Statistical analyses were performed to rank frequency bands contributing toward the prediction of HPV status. An outcome‐driven frequency filter was designed accordingly and applied to GTV frequency information. A 3D inverse discrete Fourier transform was applied to reconstruct HPV‐related frequency‐filtered images. Radiomics features (11 feature‐categories) were extracted from pre‐ and post‐frequency filtered images using our previously published “ROdiomiX” software. Least‐absolute‐shrinkage‐and‐selection‐operation (Lasso) combined with a generalized linear model (Lasso‐GLM) was developed to identify and rank feature subsets with the optimal information for prediction of HPV+/–. Radiomics‐based Lasso‐GLM classifiers (pre‐ and post‐frequency filtered) were constructed and validated using random permutation sampling and nested cross‐validation (CV) techniques. Average area under the receiver operating characteristic (AUC), and positive and negative predictive values (PPV and NPV) were computed to estimate generalization error and prediction performance. Results Among 192 radiomic features, 15 features were found to be statistically significant discriminators between HPV+/– cohorts on post‐frequency filtered CE‐CT images. Fourteen such radiomic features were observed on pre‐frequency filtered datasets. Discriminant features included tumor morphology and intensity contrast. Performances for prediction of HPV for the pre‐ and post‐frequency filtered Lasso‐GLM classifiers were as follows: AUC/PPV/NPV = 0.789/0.755/0.805 and 0.850/0.808/0.877, respectively. Nested CV performances for prediction of HPV for the pre‐ and post‐frequency filtered Lasso‐GLM classifiers were as follows: AUC/PPV/NPV = 0.814/0.725/0.877 and 0.890/0.820/0.911, respectively. Conclusion Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results on the importance of frequency analysis prior to radiomic feature extraction toward enhancement of model performance for characterizing HPV in patients with OPSCC.

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2D fourier transform for global analysis and classification of meibomian gland images

Cited by 14 publications

References 25 publications

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

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

Imaging the tarsal plate: A Mini-Review

Technical note: On the development of an outcome‐driven frequency filter for improving radiomics‐based modeling of human papillomavirus (HPV) in patients with oropharyngeal squamous cell carcinoma

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