Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva

Conjunctival hyperemia or conjunctival redness is a symptom that can be associated with a broad group of ocular diseases. Its levels of severity are represented by standard photographic charts that are visually compared with the patient’s eye. This way, the hyperemia diagnosis becomes a nonrepeatable task that depends on the experience of the grader. To solve this problem, we have proposed a computer-aided methodology that comprises three main stages: the segmentation of the conjunctiva, the extraction of features in this region based on colour and the presence of blood vessels, and, finally, the transformation of these features into grading scale values by means of regression techniques. However, the conjunctival segmentation can be slightly inaccurate mainly due to illumination issues. In this work, we analyse the relevance of different features with respect to their location within the conjunctiva in order to delimit a reliable region of interest for the grading. The results show that the automatic procedure behaves like an expert using only a limited region of interest within the conjunctivaThis research has been partially supported by the Ministerio de Economía y Competitividad through the Research Contract DPI2015-69948-R. María Luisa Sánchez Brea acknowledges the support of the University of A Coruna though the Inditex-UDC Grant ProgramS

A fully automated pipeline for a robust conjunctival hyperemia estimation

Purpose: Many semi-automated and fully-automated approaches have been proposed in literature to improve the objectivity of the estimation of conjunctival hyperemia, based on image processing analysis of eyes’ photographs. The purpose is to improve its evaluation using faster fully-automated systems and independent by the human subjectivity. Methods: In this work, we introduce a fully-automated analysis of the redness grading scales able to completely automatize the clinical procedure, starting from the acquired image to the redness estimation. In particular, we introduce a neural network model for the conjunctival segmentation followed by an image processing pipeline for the vessels network segmentation. From these steps, we extract some features already known in literature and whose correlation with the conjunctival redness has already been proved. Lastly, we implemented a predictive model for the conjunctival hyperemia using these features. Results: In this work, we used a dataset of images acquired during clinical practice.We trained a neural network model for the conjunctival segmentation, obtaining an average accuracy of 0.94 and a corresponding IoU score of 0.88 on a test set of images. The set of features extracted on these ROIs is able to correctly predict the Efron scale values with a Spearman’s correlation coefficient of 0.701 on a set of not previously used samples. Conclusions: The robustness of our pipeline confirms its possible usage in a clinical practice as a viable decision support system for the ophthalmologists

Automatic grading of ocular hyperaemia using image processing techniques

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] The human eye is affected by a number of high-prevalence pathologies, such as Dry Eye Syndrome or allergic conjunctivitis. One of the symptoms that these health problems have in common is the occurrence of hyperaemia in the bulbar conjunctiva, as a consequence of blood vessels getting clogged. The blood is trapped in the affected area and some visible signs, such an increase in the redness of the area, appear. This work proposes an automatic methodology for bulbar hyperaemia grading based on image processing and machine learning techniques. The methodology receives a video as input, chooses the best frame of the sequence, isolates the conjunctiva, computes several image features and, finally, transforms these features to the ranges that optometrists use to evaluate the parameter. Moreover, several tests have been conducted in order to analyse how the methodology reacts to unfavourable situations. The goal was to cover some common issues that assisted diagnosis methodologies have to face in real-world environments. The proposed methodology achieves a significant reduction of the time that the specialists have to invest in the evaluation. Thus, it has a direct repercussion on reaching a fast diagnosis. Moreover, it removes the inherent subjectivity of the manual process and ensures its repeatability. As a consequence, the experts can gain insight in the parameters that influence hyperaemia evaluation.[Resumen] El ojo humano se ve afectado por un gran número de patologías de alta prevalencia, tales corno el Síndrome del Ojo Seco o la conjuntivitis alérgica. Uno de los síntomas que estos problemas de salud comparten es la aparición de hiperemia en la conjuntiva bulbar, consecuencia del taponamiento de vasos sanguíneos. La sangre queda atrapada en el área afectada y aparecen signos visibles, como el aumento de rojez en la zona. Este trabajo propone una metodología automática para la evaluación de hiperemia bulbar basada en técnicas de procesado de imagen y aprendizaje máquina. La metodología recibe un vídeo, escoge la mejor imagen de la secuencia, aísla la conjuntiva, calcula varias características en la imagen y, por último, transforma estas características al rango de valores que los optometristas usan para evaluar la hiperemia. Además, se han realizado varias pruebas para analizar como reacciona la metodología a situaciones desfavorables. El objetivo era incluir problemas comunes que aparecen a la hora de aplicar una metodología de asistencia al diagnóstico en un entorno real. La metodología propuesta consigue una reducción significativa del tiempo que los especialistas invierten en la evaluación. Por lo tanto, tiene repercusiones directas en alcanzar un diagnóstico rápido. Además, elimina la subjetividad inherente al proceso manual y garantiza su repetitibilidad. Como consecuencia, los expertos pueden obtener información acerca de los parámetros involucrados en la evaluación de la hiperemia.[Resumo] O ollo humano vese afectado por un elevado número de patoloxías de alta prevalencia, tales como o Síndrome do Olio Seco ou a conxuntivite alérxica. Un dos síntomas que ditos problemas de saúde teñen en común é a aparición de hiperemia na conxuntiva bulbar, consecuencia da conxestíón dos vasos sanguíneos. O sangue queda atrapado na área afectada, e aparecen signos visibles, como o incremento do arrubiamento na zona. Este traballo propón unha metodoloxia automática para a avaliación da hiperemia bulbar baseada en técnicas de procesado de imaxe e aprendizaxe máquina. A metodoloxía recibe un video como entrada, escolle a mellor imaxe da secuencia, illa a conxuntiva, calcula varias características da imaxe e, por último, transforma estas características ós rangos que os optometristas usan para avaHar o parámetro. Ademáis, leváronse a cabo varias probas para analizar como reacciona a metodoloxía ante situacións pouco favorables. O obxectivo era abarcar algúns dos problemas máis comúns que atopan as metodoloxías de asistencia á diagnose en entornos reais. A metodoloxía proposta consegue milla redución significativa do tempo que os especialistas invirten na avaliación. Polo tanto, ten unha repercusión directa na obtención dunha diagnose rápida. Ademáis, elimina a subxectividade inerente ó proceso manual, e asegura a súa repetitibilidade. Como consecuencia, os expertos poden entender mellor os parámetros que influencian a avaliación da hiperemia

Consideration of canny edge detection for eye redness image processing: a review

Eye redness can be taken as a sign of inflammation which may suggest severity and progression of a specific disease. In image processing, there is apportioning a digital image into relevant features in sets of pixels where is called image segmentation. The image that consists of numerous parts of different colors and textures need to be distinguished in this process. In each digital image, the transformation of images into edges was using edge detection techniques. It represents the contour of the image which could be helpful to recognize the image as an object with its detected edges. The Canny edge detector is a standard edge detection algorithm for many years among the present edge detection algorithms. This paper focuses on important canny edge detection for detecting a region of interest (ROI) in eye redness images

Meibomian gland secretion quality association with ocular parameters in university students during COVID- 19 restrictions

Purpose: To determine if the Meibomian Gland (MG) secretion quality is associated with symptoms of ocular discomfort, hours of Video Display Terminals (VDT) use, eyelid margin abnormalities, conjunctival hyperemia, and Meibomian Gland Loss Area (MGLA) in a sample of university students. Methods: An online survey that included an Ocular Surface Disease Index (OSDI) questionnaire and an extra question about hours of VDT use recruited an initial sample of 183 participants. Only 120 participants that fulfilled the inclusion criteria were scheduled for a battery of ocular surface and MG specific exam. The tests include: 1) meibometry, 2) slit lamp exploration of eyelid margin abnormalities (irregularity, hyperemia and MG orifices plugging), MG secretion quality and conjunctival hyperemia, and 3) Meibography. Results: Significant positive correlations between the MG secretion quality and eyelid margin hyperemia, MG orifices plugging, MGLA, nasal conjunctival hyperemia, and temporal conjunctival hyperemia (Spearman Rho; all r>0.186, p<0.042) were found. Multivariate regression found association between OSDI with hours of VDT use (B=0.316, p=0.007), and eyelid hyperemia (B=0.434, p≤ 0.001). A statistical association between MG secretion quality and eyelid margin hyperemia, MG orifices plugging, MGLA and conjunctival hyperemia (Fisher’s exact; all p<0.039) were found. Multivariate regression found association between MG secretion quality with MG orifices plugging (B=0.295, p=0.004) and meibometry (B=-0.001, p=0.029). Conclusion: Participants with higher values in MG secretion quality have higher values in eyelid margin hyperemia, MG plugging, MGLA, and conjunctival hyperemia. No direct relationship between MG secretion quality and hours of VDT use or OSDI were found.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureS

Reliability of pterygium redness grading software (PRGS) in describing different types of primary pterygia based on appearance

The aim of this study was to evaluate the reliability of Pterygium Redness Grading Software (PRGS) in describing different types of primary pterygia. Ninety-three participants with primary pterygia who visited an ophthalmology clinic were recruited in this study. PRGS is a semi-automated computer program used to measure fibrovascular pterygium redness by analysing digital images of the pterygium and grading it on a continuous scale of 1 (minimum redness) to 3 (maximum redness). An ocular surface expert graded all 93 images in random order. The reliability of PRGS was determined by comparing pterygium redness measured using the software and by the expert. The mean and standard deviation of redness of the pterygium fibrovascular images measured using PRGS and by the expert were 1.81 ± 0.58 and 1.73 ± 0.61, respectively (P = 0.396). A comparative analysis based on pterygium type showed an increase in redness according to pterygium type (Type I: 1.43 ± 0.32; Type II: 1.67 ± 0.55; and Type III: 2.31 ± 0.46), without significant differences compared to redness measured by the expert (Type I: 1.38 ± 0.34; Type II: 1.78 ± 0.62; and Type III: 2.02 ± 0.66) (all P > 0.05). PRGS could describe and classify pterygia according to their redness, and PRGS-based classification was in agreement with the established classification of pterygia. Therefore, PRGS can be used in addition to the existing pterygium grading system

Reliability of Pterygium Redness Grading Software (PRGS) in describing different types of primary pterygia based on appearance = Kebolehpercayaan Perisian Penggredan Kemerahan Pterigium (PRGS) dalam mengelaskan pelbagai jenis pterigium berdasarkan perawakan

he aim of this study was to evaluate the reliability of Pterygium Redness Grading Software (PRGS) in describing different types of primary pterygia. Ninety-three participants with primary pterygia who visited an ophthalmology clinic were recruited in this study. PRGS is a semi-automated computer program used to measure fibrovascular pterygium redness by analysing digital images of the pterygium and grading it on a continuous scale of 1 (minimum redness) to 3 (maximum redness). An ocular surface expert graded all 93 images in random order. The reliability of PRGS was determined by comparing pterygium redness measured using the software and by the expert. The mean and standard deviation of redness of pterygium fibrovascular images measured using PRGS and by the expert were 1.81 ± 0.58 and 1.73 ± 0.61, respectively (P = 0.396). A comparative analysis based on pterygium type showed an increase in redness according to pterygium type (Type I: 1.43 ± 0.32; Type II: 1.67 ± 0.55; and Type III: 2.31 ± 0.46), without significant differences compared to redness measured by the expert (Type I: 1.38 ± 0.34; Type II: 1.78 ± 0.62; and Type III: 2.02 ± 0.66) (all P > 0.05). PRGS could describe and classify pterygia according to their redness, and PRGS-based classification was in agreement with the the established classification of pterygia. Therefore, PRGS can be used in addition to the existing pterygium grading system. ******************************************************************************* Matlamat kajian ini adalah untuk menilai kebolehpercayaan Perisian Penggredan Kemerahan Pterygium (PRGS) dalam mengelaskan jenis-jenis pterigium primer. Kajian ini berjaya merekrut 93 pesakit daripada klinik oftalmologi yang menghidap pterigium primer. PRGS merupakan program komputer semi-automatik yang berfungsi untuk mengukur darjah kemerahan pterigium fibrovaskular yang diperoleh daripada imej digital pterigium, dalam bentuk pengredan berterusan (1 untuk kemerahan minimum dan 3 untuk kemerahan maksimum). Kesemua 93 imej pterigium telah digredkan secara rambang oleh pakar permukaan okul. Kebolehpercayaan PRGS telahpun ditentukan dengan membandingkannya dengan kemerahan yang dicerap oleh pakar. Nilai min dan sisihan piawai untuk kemerahan pterigium fibrovaskular adalah 1.81 ± 0.58 (PRGS) dan 1.73 ± 0.61 (pakar), (P = 0.396). Analisis berasaskan jenis pterigium menunjukkan terdapat peningkatan kemerahan pterigium fibrovaskular apabila diukur menggunakan PRGS (Jenis I: 1.43 ± 0.32; Jenis II: 1.67 ± 0.55; Jenis III: 2.31 ± 0.46) berbanding pakar (Jenis I: 1.38 ± 0.34; Jenis II: 1.78 ± 0.62; Jenis III: 2.02 ± 0.66), tetapi perbezaan ini tidak signifikan untuk semua jenis pterigium (P > 0.05). Skala pengredan PRGS dapat mengelaskan pterigium berdasarkan kemerahan dan ia selaras dengan pengelasan pterigium sedia ada. Skala kemerahan ini boleh digunakan sebagai tambahan kepada pengredan pterigium yang sedia ada

The evaluation of bulbar redness grading scales

The use of grading scales is common in clinical practice and research settings. A number of grading scales are available to the practitioner, however, despite their frequent use, they are only poorly understood and may be criticised for a number of things such as the variability of the assessments or the inequality of scale steps within or between scales. Hence, the global aim of this thesis was to study the McMonnies/Chapman-Davies (MC-D), Institute for Eye Research (IER), Efron, and validated bulbar redness (VBR) grading scales in order to (1) get a better understanding and (2) attempt a cross-calibration of the scales. After verifying the accuracy and precision of the objective and subjective techniques to be used (chapter 3), a series of experiments was conducted. The specific aims of this thesis were as follows: • Chapter 4: To use physical attributes of redness to determine the accuracy of the four bulbar redness grading scales. • Chapter 5: To use psychophysical scaling to estimate the perceived redness of the four bulbar redness grading scales. • Chapter 6: To investigate the effect of using reference anchors when scaling the grading scale images, and to convert grades between scales. • Chapter 7: To grade bulbar redness using cross-calibrated versions of the MC-D, IER, Efron, and VBR grading scales. Methods: • Chapter 4: Two image processing metrics, fractal dimension (D) and % pixel coverage (% PC), as well as photometric chromaticity (u’) were selected as physical measures to describe and compare redness in the four bulbar redness grading scales. Pearson correlation coefficients were calculated between each set of image metrics and the reference image grades to determine the accuracy of the scales. • Chapter 5: Ten naïve observers were asked to arrange printed copies of modified versions of the reference images (showing vascular detail only) across a distance of 1.5m for which only start and end point were indicated by 0 and 100, respectively (non-anchored scaling). After completion of scaling, the position of each image was hypothesised to reflect its perceived bulbar redness. The averaged perceived redness (across observers) for each image was used for comparison to the physical attributes of redness as determined in chapter 4. • Chapter 6: The experimental setup from chapter 5 was modified by providing the reference images of the VBR scale as additional, unlabelled anchors for psychophysical scaling (anchored scaling). Averaged perceived redness from anchored scaling was compared to non-anchored scaling, and perceived redness from anchored scaling was used to cross-calibrate grades between scales. • Chapter 7: The modified reference images of each grading scale were positioned within the 0 to 100 range according to their averaged perceived redness from anchored scaling, one scale at a time. The same 10 observers who had participated in the scaling experiments were asked to represent perceived bulbar redness of 16 sample images by placing them, one at a time, relative to the reference images of each scale. Perceived redness was taken as the measured position of the placed image from 0 and was averaged across observers. Results: • Chapter 4: Correlations were high between reference image grades and all sets of objective metrics (all Pearson’s r’s≥0.88, p≤0.05); each physical attribute pointed to a different scale as being most accurate. Independent of the physical attribute used, there were wide discrepancies between scale grades, with sometimes little overlap of equivalent levels when comparing the scales. • Chapter 5: The perceived redness of the reference images within each scale was ordered as expected, but not all consecutive within-scale levels were rated as having different redness. Perceived redness of the reference images varied between scales, with different ranges of severity being covered by the images. The perceived redness was strongly associated with the physical attributes of the reference images. • Chapter 6: There were differences in perceived redness range and when comparing reference levels between scales. Anchored scaling resulted in an apparent shift to lower perceived redness for all but one reference image compared to non-anchored scaling, with the rank order of the 20 images for both procedures remaining fairly constant (Spearman’s ρ=0.99). • Chapter 7: Overall, perceived redness depended on the sample image and the reference scale used (RM ANOVA; p=0.0008); 6 of the 16 images had a perceived redness that was significantly different between at least two of the scales. Between-scale correlation coefficients of concordance (CCC) ranged from 0.93 (IER vs. Efron) to 0.98 (VBR vs. Efron). Between-scale coefficients of repeatability (COR) ranged from 5 units (IER vs. VBR) to 8 units (IER vs. Efron) for the 0 to 100 range. Conclusions: • Chapter 4: Despite the generally strong linear associations between the physical characteristics of reference images in each scale, the scales themselves are not inherently accurate and are too different to allow for cross-calibration based on physical redness attributes. • Chapter 5: Subjective estimates of redness are based on a combination of chromaticity and vessel-based components. Psychophysical scaling of perceived redness lends itself to being used to cross calibrate the four clinical scales. • Chapter 6: The re-scaling of the reference images with anchored scaling suggests that redness was assessed based on within-scale characteristics and not using absolute redness scores, a mechanism that may be referred to as clinical scale constancy. The perceived redness data allow practitioners to modify the grades of the scale they commonly use so that comparisons of grading estimates between calibrated scales may be made. • Chapter 7: The use of the newly calibrated reference grades showed close agreement between grading estimates of all scales. The between-scale variability was similar to the variability typically observed when a single scale is repeatedly used. Perceived redness appears to be dependent upon the dynamic range of the reference images of the scale. In conclusion, this research showed that there are physical and perceptual differences between the reference images of all scales. A cross-calibration of the scales based on the perceived redness of the reference images provides practitioners with an opportunity to compare grades across scales, which is of particular value in research settings or if the same patient is seen by multiple practitioners who are familiar with using different scales

Dry Eye Syndrome

Dry eye syndrome is one of the most common types of ocular surface disorders that significantly worsen the quality of life of tens of millions of people worldwide.In the last decades, researchers worldwide investigated the composition and regulatory mechanisms of the preocular tear film to better understand dry eye syndrome. The tear film, in fact, plays a critical role in maintaining corneal and conjunctival integrity, protecting the eyes against infections, and preserving visual acuity. Recent scientific discoveries helped us gain a more and more accurate understanding of the structure and functioning of the tear film and how disorders in the tear film relate to dry eye syndrome. Today, ophthalmologists benefit from sophisticated diagnostic techniques, and they have at their disposal a wide range of effective therapeutic options to manage dry eye syndrome. This book illustrates the most recent research results in the diagnosis and treatment of dry eye syndrome, and it is of interest to the broad audience that comprises ophthalmologists, researchers, and students

A review of artificial intelligence applications in anterior segment ocular diseases

Background: Artificial intelligence (AI) has great potential for interpreting and analyzing images and processing large amounts of data. There is a growing interest in investigating the applications of AI in anterior segment ocular diseases. This narrative review aims to assess the use of different AI-based algorithms for diagnosing and managing anterior segment entities. Methods: We reviewed the applications of different AI-based algorithms in the diagnosis and management of anterior segment entities, including keratoconus, corneal dystrophy, corneal grafts, corneal transplantation, refractive surgery, pterygium, infectious keratitis, cataracts, and disorders of the corneal nerves, conjunctiva, tear film, anterior chamber angle, and iris. The English-language databases PubMed/MEDLINE, Scopus, and Google Scholar were searched using the following keywords: artificial intelligence, deep learning, machine learning, neural network, anterior eye segment diseases, corneal disease, keratoconus, dry eye, refractive surgery, pterygium, infectious keratitis, anterior chamber, and cataract. Relevant articles were compared based on the use of AI models in the diagnosis and treatment of anterior segment diseases. Furthermore, we prepared a summary of the diagnostic performance of the AI-based methods for anterior segment ocular entities. Results: Various AI methods based on deep and machine learning can analyze data obtained from corneal imaging modalities with acceptable diagnostic performance. Currently, complicated and time-consuming manual methods are available for diagnosing and treating eye diseases. However, AI methods could save time and prevent vision impairment in eyes with anterior segment diseases. Because many anterior segment diseases can cause irreversible complications and even vision loss, sufficient confidence in the results obtained from the designed model is crucial for decision-making by experts. Conclusions: AI-based models could be used as surrogates for analyzing manual data with improveddiagnostic performance. These methods could be reliable tools for diagnosing and managing anterior segmentocular diseases in the near future in remote areas. It is expected that future studies can design algorithms thatuse less data in a multitasking manner for the detection and management of anterior segment diseases