Computer Vision Based Early Intraocular Pressure Assessment From Frontal Eye Images

Intraocular Pressure (IOP) in general, refers to the pressure in the eyes. Gradual increase of IOP and high IOP are conditions or symptoms that may lead to certain diseases such as glaucoma, and therefore, must be closely monitored. While the pressure in the eye increases, different parts of the eye may become affected until the eye parts are damaged. An effective way to prevent rise in eye pressure is by early detection. Exiting IOP monitoring tools include eye tests at clinical facilities and computer-aided techniques from fundus and optic nerves images. In this work, a new computer vision-based smart healthcare framework is presented to evaluate the intraocular pressure risk from frontal eye images early-on. The framework determines the status of IOP by analyzing frontal eye images using image processing and machine learning techniques. A database of images from the Princess Basma Hospital was used in this work. The database contains 400 eye images; 200 images with normal IOP and 200 high eye pressure case images. This study proposes novel features for IOP determination from two experiments. The first experiment extracts the sclera using circular hough transform, after which four features are extracted from the whole sclera. These features are mean redness level, red area percentage, contour area and contour height. The pupil/iris diameter ratio feature is also extracted from the frontal eye image after a series of pre-processing techniques. The second experiment extracts the sclera and iris segment using a fully conventional neural network technique, after which six features are extracted from only part of the segmented sclera and iris. The features include mean redness level, red area percentage, contour area, contour distance and contour angle along with the pupil/iris diameter ratio. Once the features are extracted, classification techniques are applied in order to train and test the images and features to obtain the status of the patients in terms of eye pressure. For the first experiment, neural network and support vector machine algorithms were adopted in order to detect the status of intraocular pressure. The second experiment adopted support vector machine and decision tree algorithms to detect the status of intraocular pressure. For both experiments, the framework detects the status of IOP (normal or high IOP) with high accuracies. This computer vison-based approach produces evidence of the relationship between the extracted frontal eye image features and IOP, which has not been previously investigated through automated image processing and machine learning techniques from frontal eye images

Classification of Humans into Ayurvedic Prakruti Types using Computer Vision

Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine. This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda

Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

Purpose:To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression. Methods:Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 healthy participants (56 eyes) and 93 glaucoma participants (179 eyes). RNFL thickness maps were extracted from segmented SS-OCT images and an unsupervised machine learning approach based on principal component analysis (PCA) was used to identify novel structural features. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic accuracy of RNFL PCA for detecting glaucoma and progression compared to SAP, FDT, and cpRNFL measures. Results:The RNFL PCA features were significantly associated with mean deviation (MD) in both SAP (R2 = 0.49, P < 0.0001) and FDT visual field testing (R2 = 0.48, P < 0.0001), and with mean circumpapillary RNFL thickness (cpRNFLt) from SD-OCT (R2 = 0.58, P < 0.0001). The identified features outperformed each of these measures in detecting glaucoma with an AUC of 0.95 for RNFL PCA compared to an 0.90 for mean cpRNFLt (P = 0.09), 0.86 for SAP MD (P = 0.034), and 0.83 for FDT MD (P = 0.021). Accuracy in predicting progression was also significantly higher for RNFL PCA compared to SAP MD, FDT MD, and mean cpRNFLt (P = 0.046, P = 0.007, and P = 0.044, respectively). Conclusions:A computational approach can identify structural features that improve glaucoma detection and progression prediction