Local information pattern descriptor for corneal diseases diagnosis

Abstract

<span lang="EN-US">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.</span>

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

“…Therefore, the NIR channel was used to refine the image's contrast. The HE method enhances the image by distributing the image brightness levels equally across the brightness scale [1], [11], [15], [21], [44], [53], [54], [72], [94], [119], [136], [140], [145], [150], [159], [168], [169], [172], [175]. Furthermore, the intensity of the contrast enhancement method is measured through the root mean square (RMS), where the higher the RMS value, the better the contrast image [22], [35], [48], [178]- [181].…”

“…Feature extraction allows for a more-accurate detection of features, as a result of the image's enhancements [15], [16], [39], [89], [143]- [145], [148]. The signature of feature classes could be distinguished from complex surface textures.…”

Large scale data analysis using MLlib

“…As well as enabling the implementation of numerous machine learning strategies, such as grouping and regression; classification; rule extraction; and dimensional reduction. It also enables the rapid and simple creation of machine learning approaches for large-scale applications [67], [111]- [116].…”

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

“…Any disorders in the cornea may cause ripping, discomfort, and dwindling vision clarity and, finally, may lead to blindness. On the other hand, any action on the cornea, such as vision correction, requires a diagnosis of the cornea’s health before treatment [ 22 ]. Clinical decisions on the human cornea require reviewing numerous aspects, and ophthalmologists must handle this revision.…”

“…In 2021, Samer and his participants designed an LIP algorithm to extract corneal image features, and they evaluated their method using many classifiers. Thus, they could train a system capable of automatically classifying corneal diseases [ 22 ]. We used deep learning techniques in the current study to diagnose corneal diseases.…”