Beatriz Remeseiro

Automatic classification of the interferential tear film lipid layer using colour texture analysis

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. This papers presents an exhaustive study about the characterisation of the interference phenomena as a texture pattern, using different feature extraction methods in different colour spaces. These methods are first analysed individually and then combined to achieve the best results possible. The principal component analysis (PCA) technique has also been tested to reduce the dimensionality of the feature vectors. The proposed methodologies have been tested on a dataset composed of 105 images from healthy subjects, with a classification rate of over 95% in some cases.

Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper presents an exhaustive study about the problem at hand using different texture analysis methods in three colour spaces and different machine learning algorithms. All these methods and classifiers have been tested on a dataset composed of 105 images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated with the benefits of being faster and unaffected by subjective factors, with maximum accuracy over 95%.

Texture and color analysis for the automatic classification of the eye lipid layer

This paper describes a methodology for the automatic classification of the eye lipid layer based on the categories enumerated by Guillon [1]. From a photography of the eye, the system detects the region of interest where the analysis will take place, extracts its low-level features, generates a feature vector that describes it and classifies the feature vector in one of the target categories. We have tested our methodology on a dataset composed of 105 images, with a classification rate of over

Colour Texture Analysis for Classifying the Tear Film Lipid Layer: A Comparative Study

This paper presents a comparative study of different texture extraction methods for the automatic classification of the tear film lipid layer based on the categories enumerated by Guillon. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper discusses several texture analysis methods and colour spaces to generate the feature vectors. The proposed methods have been tested on a dataset composed of 105 images, with a classification rate of over 95\% in some cases.

A Methodology for Improving Tear Film Lipid Layer Classification

Dry eye is a symptomatic disease which affects a wide range of population and has a negative impact on their daily activities. Its diagnosis can be achieved by analyzing the interference patterns of the tear film lipid layer and by classifying them into one of the Guillon categories. The manual process done by experts is not only affected by subjective factors but is also very time consuming. In this paper we propose a general methodology to the automatic classification of tear film lipid layer, using color and texture information to characterize the image and feature selection methods to reduce the processing time. The adequacy of the proposed methodology was demonstrated since it achieves classification rates over 97% while maintaining robustness and provides unbiased results. Also, it can be applied in real time, and so allows important time savings for the experts.

Automatic cyst detection in OCT retinal images combining region flooding and texture analysis

In this work Optical Coherence Tomography (OCT) retinal images are automatically processed to detect the presence of cysts. The methodology is composed by three phases: region of interest where cysts will be searched is delimited; a watershed algorithm is applied to find all the possible regions in the image which might conform cystic structures; finally, texture analysis is performed in each region from previous phase to final classification. Results show that accuracy achieved with this method is over 80%.

Color texture analysis for tear film classification: a preliminary study

The tear lipid layer is not homogeneous among the population and its classification depends on its width. Too thin or too thick films can lead to unhealthy eyes as well as create problems when interacting with contact lenses. This work proposes a preliminary methodology to classify the tear lipid layer according to its texture into four main categories. The proposed methodology works on several stages to detect the region of interest, extract the texture descriptors on colour information and classify these descriptors. The method has been tested on several images from each tear type. In some cases, we obtain classification results over the 90%.

Correlation between tear osmolarity and tear meniscus.

Purpose To examine the relationship between tear meniscus height (TMH) and subjective meniscus grading (subjective tear meniscus [TM]) with tear osmolarity. Methods Tear osmolarity measurements (using TearLab) and digital images of the TM were obtained in 177 consecutive patients undergoing an eye examination at our optometry clinic (Universidad de Santiago de Compostela, Spain) who fulfilled the study’s inclusion criteria. Participants were also administered the McMonnies and Ocular Surface Disease Index questionnaires for the detection of dry eye disease. The lower TM was videotaped by a digital camera attached to a slit lamp in its central portion without fluorescein instillation. After the study, a masked observer extracted an image from each video and measured the TMH using open source software (NIH ImageJ). Subsequently, the masked observer subjectively graded the appearance of each meniscus. For statistical analysis, subjects were stratified by age and by dry eye symptoms as indicated by their scores in the two questionnaires. Results In the whole study population, a significant relationship was observed between osmolarity and TMH (r = −0.41, p < 0.001) and osmolarity and subjective TM (r = 0.35, p < 0.001). A cluster analysis revealed similar correlations when subjects were stratified by age or dry eye symptoms, these correlations being more pronounced in older and more symptomatic subjects. Objective TMH measurements and subjective meniscus quality were also correlated (r = −0.75, p < 0.001). Conclusions Osmolarity and both objective TMH measurements and subjective interpretation of the meniscus showed high correlation, especially in older symptomatic subjects.

Automatic Drusen Detection from Digital Retinal Images: AMD Prevention

The age-related macular degeneration (AMD) is the main cause of blindness among people over 50 years in developed countries and there are 150 million people affected worlwide. This disease can lead to severe loss central vision and adversely affect the patients quality of life. The appearance of drusen is associated with the early AMD, so we proposed a top-down methodology to detect drusen in initial stages to prevent AMD. The proposed methodology has several stages where the key issues are the detection and characterization of suspect areas. We test our method with a set of 1280 ?1024 images, obtaining a system with a high sensitivity in the localization of drusen, not just fake injuries.

Tear Film Maps based on the Lipid Interference Patterns

Dry eye syndrome is characterized by symptoms of discomfort, ocular surface damage, reduced tear film stability, and tear hyperosmolarity. These features can be identified by several types of diagnostic tests, although there may not be a direct correlation between the severity of symptoms and the degree of damage. One of the most used clinical tests is the analysis of the lipid interference patterns, which can be observed on the tear film, and their classification into the Guillon categories. Our previous researches have demonstrated that the interference patterns can be characterized as color texture patterns. Thus, the manual test done by experts can be performed through an automatic process which saves time for experts and provides unbiased results. Nevertheless, the heterogeneity of the tear film makes the classification of a patients image into a single category impossible. For this reason, this paper presents a methodology to create tear film maps based on the lipid interference patterns. In this way, the output image represents the distribution and prevalence of the Guillon categories on the tear film. The adequacy of the proposed methodology was demonstrated since it achieves reliable results in comparison with the annotations done by experts.