Vessel enhancement of low quality fundus image using mathematical morphology and combination of Gabor and matched filter

Abstract

Fundus images have been widely used in the diagnosis of retinopathy and cardiovascular diseases. However, because of the movement of patients' eyes and limitation of medical equipments, the quality of fundus images may be low sometimes. In this paper, we propose vessel enhancement method for a low-contrast and blurred image based on multi-scale morphological top-hat transformation, and the combination of Gabor and matched filter. The underlying rationale of this proposed method is to make use different kinds of information to improve blood vessels on retinal fundus images with poor quality. Experiments show that vessels enhanced by our proposed method are much clearer than the ones using original multi-scale morphological top-hat transformation, Gabor filter or matched filter. The results confirm that the quality of vessel enhancement can be improved by combining these methods.

Lab Members

Patrick Chan

Associate Professor, Vice Dean

Patrick Chan works on machine learning, deep learning, image processing, adversarial learning, and secure machine learning.