Local information pattern descriptor for corneal diseases diagnosis

Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting

A Review of Lower Limb Exoskeletons

In general, exoskeletons are defined as wearable robotic mechanisms for providing mobility. In the last six decades, many research work have been achieved to enhance the performance of exoskeletons thus developing them to nearly commercialized products. In this paper, a review is made for the lower limb exoskeleton concerning history, classification, selection and development, also a discussion for the most important aspects of comparison between different designs is presented. Further, some concluding remarks are withdrawn which could be useful for future work. Keywords: Exoskeletons, Lower extremity exoskeleton, Wearable robot

Thermal Topographical Rings as a New Tool for Laser Eye Surgery

Abstract Measurement of the corneal surface temperature during the laser surgery have been modified at the last few years, to be used as an extra useful monitoring tool during the dynamic ablation process. While the concentric Placido rings have been used before to measure the refractive errors, here, it have been modified to be used as a new suggested tool to study the thermal response upon the anterior corneal surface during laser eye surgeries. The thermal infrared camera was used to get an image captured at the end of the treatment, where contours with isotherms are derived and examined. The new contour lines introduce the temperature induced per location upon the corneal surface and reflect the biomechanical response behavior. Comparing the contour image with the image generated by the treatment system for the ablated depth showed a new indication for safety limits especially the effect of decentration and other irregular aberrations

Machine learning techniques for corneal diseases diagnosis: A survey

*Jameel, Samer Kais ( Aksaray, Yazar )Machine learning techniques become more related to medical researches by using medical images as a dataset. It is categorized and analyzed for ultimate effectiveness in diagnosis or decision-making for diseases. Machine learning techniques have been exploited in numerous researches related to corneal diseases, contribution to ophthalmologists for diagnosing the diseases and comprehending the way automated learning techniques act. Nevertheless, confusion still exists in the type of data used, whether it is images, data extracted from images or clinical data, the course reliant on the type of device for obtaining them. In this study, the researches that used machine learning were reviewed and classified in terms of the kind of utilized machine for capturing data, along with the latest updates in sophisticated approaches for corneal disease diagnostic techniques

SWFT: Subbands wavelet for local features transform descriptor for corneal diseases diagnosis

Al-Salihi, Samer K. (Aksaray, Yazar )Human cornea is the front see-through shield of the eye. It refracts light onto the retina to induce vision. Therefore, any defect in the cornea may lead to vision disturbance. This deficiency is estimated by sets of topographical images measured, and assessed by an ophthalmologist. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms. Images produced by a Pentacam device can be subjected to rotation or some distortion during acquisition; therefore, accurate diagnosis requires the use of local features in the image. Accordingly, a new algorithm called subbands wavelet for local features transform (SWFT) which is mainly based on the algorithm of a scale-invariant feature transform (SIFT) has been developed. This algorithm uses wavelets as a multiresolution analysis to produce images with different scales instead of using the difference of Gaussians as in the SIFT algorithm. The experimental results on the corneal topography dataset indicate that the proposed SWFT outperforms the baseline SIFT algorithm

Design and Development of A New Portable Roof Gutter for Electricity Production

Despite a sufficient energy supply, harvesting energy from rainfall is essential for intelligent water management. A significant part is still untapped or little exploited, which is the renewable energy produced from rainwater. This paper proposes a portable gutter of the rainwater energy harvesting system to provide electricity that may be sufficient for powering lights and charging cell phones in rainy locations with limited electricity. A prototype is designed and tested to determine the feasibility of rainwater as a source of renewable energy. The aim is to minimize and respectively suspend the use of fossil energy sources, as well as decrease the percentage of pollution as it is a cause of global warming. The system prototype consisted of a gutter assembly that collected and funneled water from the roof to a downspout. The turbine was connected through a gearbox to a DC motor serving as the generator. The device is optimal during high rainfall intensities that produce larger flow rates. A smart algorithm has been applied, which is salutary to keep the system working and has the ability to control the flow of collected rainfall water. Also, this system is useful to install and use in the rural area where the national grids are not common and the level of rainfall is high. The applied system utilized and installed in more than one hundred premises can produce more than 4 kWh for one rain. In some countries such as Malaysia, the average number of rainy days is 250 days a year, so the use of this system in 100 premises can help to provide 80 MWh to the national grid yearly. The system is characterized by simplicity of design and lack of complexity in addition to ease of installation and cheapness, which is the basis for the availability of this system for use by everyon

Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image

Corneal diseases are the most common eye disorders. Deep learning techniques are used to perform automated diagnoses of cornea. Deep learning networks require large-scale annotated datasets, which is conceded as a weakness of deep learning. In this work, a method for synthesizing medical images using conditional generative adversarial networks (CGANs), is presented. It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis. The study includes using corneal topography captured using a Pentacam device from patients with corneal diseases. The dataset contained 3448 different corneal images. Furthermore, it shows how an unbalanced dataset affects the performance of classifiers, where the data are balanced using the resampling approach. Finally, the results obtained from CNN networks trained on the balanced dataset are compared to those obtained from CNN networks trained on the imbalanced dataset. For performance, the system estimated the diagnosis accuracy, precision, and F1-score metrics. Lastly, some generated images were shown to an expert for evaluation and to see how well experts could identify the type of image and its condition. The expert recognized the image as useful for medical diagnosis and for determining the severity class according to the shape and values, by generating images based on real cases that could be used as new different stages of illness between healthy and unhealthy patients

A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.</p