Retinal vasculature enhancement using independent component analysis

Abstract

Retinal vasculature is a network of vessels in the retinal layer. In ophthalmology, information of reti- nal vasculature in analyzing fundus images is impor- tant for early detection of diseases related to the ret- ina, e.g. diabetic retinopathy. However, in fundus images the contrast between retinal vasculature and the background is very low. As a result, analyzing or visualizing tiny retinal vasculature is difficult. There- fore, enhancement of retinal vasculature in digital fundus image is important to provide better visuali- zation of retinal blood vessels as well as to increase accuracy of retinal vasculature segmentation. Fluo- rescein angiogram overcomes this imaging problem but it is an invasive procedure that leads to other physiological problems. In this research work, the low contrast problem of retinal fundus images ob- tained from fundus camera is addressed. We develop a fundus image model based on probability distribu- tion function of melanin, haemoglobin and macular pigment to represent melanin, retinal vasculature and macular region, respectively. We determine reti- nal pigments makeup, namely macular pigment, melanin and haemoglobin using independent com- ponent analysis. Independent component image due to haemoglobin obtained is used since it exhibits higher contrast retinal vasculature. Contrast of reti- nal vasculature from independent component image due to haemoglobin is compared to those from other enhancement methods. Results show that this ap- proach outperforms other non-invasive enhancement methods, such as contrast stretching, histogram eq- ualization and CLAHE and can be beneficial for retinal vasculature segmentation. Contrast enhance- ment factor up to 2.62 for a digital retinal fundus image model is achieved. This improvement in con- trast reduces the need of applying contrasting agent on patients.