Pre-processing of Retinal Images for Removal of Outliers

Abstract

Early diagnosis of diseases related with retina such as glaucoma is of utmost importance in current scenario as it is the second most prevailing cause of irreversible blindness over the world and is expected to increase further in near future. It is commonly diagnosed using retinal images which are acquired by digital fundus cameras. But the acquired images may be prone to certain outliers that create hindrance in diagnosis of glaucoma by tempering the accuracy. These outliers include retinal vessels, low contrast of images and uneven illumination that deteriorates the performance of disc and cup segmentation which are the key indicators to diagnose glaucoma. Thus, pre-processing of retinal images to remove outliers plays a significant role in diagnosis. This paper presents an approach for pre-processing the retinal fundus image followed by its comparison with state of the art. Based on the experimental analysis the performance of the proposed approach is found to be better than the state of the art based on the analysis using metrics such as peak signal to ratio, mean square error and structural similarity index. Further, the proposed approach has been compared with state of the art using metrics such as Jaccard index and dice similarity on the basis of segmentation outcomes on different pre-processing approaches.

Denoising of computed tomography using bilateral median based autoencoder network

“…These processes greatly improve the image quality for diagnosis and staging of malignancies using advanced CAD techniques like segmentation and classification. Similar studies performed by researchers using image denoising and restoration to improve the image quality for segmentation and classification are given by Juneja et al, 6 Garg et al, 7 Thakur et al, 8 and Kaur et al 9 …”

“…These processes greatly improve the image quality for diagnosis and staging of malignancies using advanced CAD techniques like segmentation and classification. Similar studies performed by researchers using image denoising and restoration to improve the image quality for segmentation and classification are given by Juneja et al, 6 Garg et al, 7 Thakur et al, 8 and Kaur et al 9 Complex imaging modality such as CT is one of the most comprehensive imaging tests used by clinicians to identify pancreatic cancer. This technique uses X-rays to get a detailed Three-dimensional (3D) image of the target organ.…”