Effectual accuracy of OCT image retinal segmentation with the aid of speckle noise reduction and boundary edge detection strategy

Optical coherence tomography (OCT) has shown to be a valuable imaging tool in the field of ophthalmology, and it is becoming increasingly relevant in the field of neurology. Several OCT image segmentation methods have been developed previously to segment retinal images, however sophisticated speckle noises with low-intensity restrictions, complex retinal tissues, and inaccurate retinal layer structure remain a challenge to perform effective retinal segmentation. Hence, in this research, complicated speckle noises are removed by using a novel far-flung ratio algorithm in which preprocessing has been done to treat the speckle noise thereby highly decreasing the speckle noise through new similarity and statistical measures. Additionally, a novel haphazard walk and inter-frame flattening algorithms have been presented to tackle the weak object boundaries in OCT images. These algorithms are effective at detecting edges and estimating minimal weighted paths to better diverge, which reduces the time complexity. In addition, the segmentation of OCT images is made simpler by using a novel N-ret layer segmentation approach that executes simultaneous segmentation of various surfaces, ensures unambiguous segmentation across neighbouring layers, and improves segmentation accuracy by using two grey scale values to construct data. Consequently, the novel work outperformed the OCT image segmentation with 98.5% of accuracy.