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.

Hemorrhages 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 within each hemorrhage is often non-uniform for the large ones. Hemorrhage segmentation in mobile-phone retinal images is even more challenging because mobile-phone retinal images usually have poorer contrast, more places of shadows, and uneven illumination compared to those obtained from the table-top ophthalmoscope. In this work, the proposed KMMRC-INRG method enhances the performance of the segmentation of the hemorrhages with the 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 Multi-Region Contrast Enhancement (KMMRC). It also enhances the segmentation of the boundary 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. We find this approach can achieve as high as 80.18%, 91.26%, and 85.36% for recall, precision, and F1-measure, 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.