Hemorrhage segmentation in mobile-phone retinal images using multiregion contrast enhancement and iterative NICK thresholding region growing

Abstract

Hemorrhage segmentation in retinal images is challenging because the sizes and shapes vary for each hemorrhage, the intensity is close to the blood vessels and macula, and the intensity is often nonuniform, especially for large hemorrhages. Hemorrhage segmentation in mobile-phone retinal images is even more challenging because mobile-phone retinal images usually have poorer contrast, more shadows, and uneven illumination compared to those obtained from the table-top ophthalmoscope. In this work, the proposed KMMRC-INRG method enhances the hemorrhage segmentation performance with nonuniform intensity in poor lighting conditions on mobile-phone images. It improves the uneven illumination of mobile-phone retinal images using a proposed method, K-mean multiregion contrast enhancement (KMMRC). It also enhances the boundary segmentation of the hemorrhage blobs using a novel iterative NICK thresholding region growing (INRG) method before applying an SVM classifier based on hue, saturation, and brightness features. This approach can achieve as high as 80.18%, 91.26%, 85.36%, and 80.08% for recall, precision, F1-measure, and IoU, respectively. The F1-measure score improves up to 19.02% compared to a state-of-the-art method DT-HSVE tested on the same full dataset and as much as 58.88% when considering only images with large-size hemorrhages.

Advancements in Cataract Detection: The Systematic Development of LeNet-Convolutional Neural Network Models

“…The work conducted by Santra et al represents a significant contribution to the field of automatic classification of optic nerve diseases, opening avenues for future research and innovation in this domain. This study [57] addresses challenging hemorrhage segmentation in retinal images, particularly on mobile phones, with poor lighting. A novel KMMRC-INRG method enhances segmentation by addressing uneven illumination using KMMRC and improving boundary segmentation through INRG.…”