Retinal image synthesis through the least action principle

Castro, Dario Lo; Valenti, Cesare; Tegolo, Domenico

doi:10.1109/iciibms50712.2020.9336421

Cited by 1 publication

(1 citation statement)

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“…The CSCR imaging method helps the eye doctor to detect and diagnose various abnormal variations of the macula. Imaging enhancement methods are also used in general medical cases, cancer, and other clinical initiatives [3].…”

Section: Introductionmentioning

confidence: 99%

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

Shoba¹,

Kumar²

2023

Computer Systems Science and Engineering

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Nowadays in the medical field, imaging techniques such as Optical Coherence Tomography (OCT) are mainly used to identify retinal diseases. In this paper, the Central Serous Chorio Retinopathy (CSCR) image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods. The first approach, which was focused on image quality, improves medical image accuracy. An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter (BADWUMF). The classifier used here is to figure out whether the OCT image is a CSCR case or not. 150 images are checked for this research work (75 abnormal from Optical Coherence Tomography Image Retinal Database, in-house clinical database, and 75 normal images). This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be minimized. The total precision is 90 percent obtained from the Two Class Support Vector Machine (TCSVM) classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale (SNNWPB) classifier using the MATLAB 2019a program.

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Section: Introductionmentioning

confidence: 99%