Width measurement for pathological vessels in retinal images using centerline correction and k-means clustering

Abstract Changes in retinal blood vessel width and morphology are closely related to the development of many diseases. However, some vessels (especially pathological vessels) often show central light reflex in retinal images, making it difficult to detect the vessel boundary and to measure the vessel width. In this study, a measuring methodology is proposed to improve the performance in measuring blood vessels with central light reflex, by use of centerline correction and k -means clustering algorithm. Firstly, the vessel tree in the retinal image is segmented using Isotropic Undecimated Wavelet Transform. Then, the centerline of each vessel is roughly extracted using morphological thinning algorithm, and corrected for vessels with central light reflex by use of a parallel line model. With known centerline, the direction and length of the measuring axis are determined, and the intensity profile of the pixels along the axis is obtained. By use of k -means clustering algorithm for the intensities, the pixels on the measuring axis are classified into vessel and background, based on which the vessel width is finally determined. The proposed method is tested on the public dataset REVIEW and our own dataset, and compared with ground truth and state-of-the-art method (ARIA method). It is shown that the measuring accuracy of proposed method is comparable to ARIA (average relative error: 3% vs. 4%), but it is more advantageous in measuring pathological vessels with central light reflex. It succeeds in measuring all the testing pathological vessels whereas ARIA is capable of measuring 20% of them. Result of the work has potential usage in quantitative evaluation of the progressing of retinal diseases.