Computerized Detection of Retina Blood Vessel Using a Piecewise Line Fitting Approach

Retina vessels are important landmarks in fundus images, an accurate segmentation of the vessels may be useful for automated screening for several eye diseases or systematic diseases, such as diebetes. A new method is presented for automated segmentation of blood vessels in two-dimensional color fundus images. First, a coherence filter and a followed mean filter are applied to the green channel of the image. The green channel is selected because the vessels have the maximal contrast at the green channel. The coherence filter is to enhance the line strength of the original image and the mean filter is to discard the intensity variance among different regions. Since the vessels are darker than the around tissues depicted on the image, the pixels with small intensity are then retained as points of interest (POI). A new line fitting algorithm is proposed to identify line-like structures in each local circle of the POI. The proposed line fitting method is less sensitive to noise compared to the least squared fitting. The fitted lines with higher scores are regarded as vessels. To evaluate the performance of the proposed method, a public available database DRIVE with 20 test images is selected for experiments. The mean accuracy on these images is 95.7% which is comparable to the state-of-art.