Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability…

• CS
  Charles Sturt University
• FD
  Fundação de Amparo à Pesquisa do Estado de São Paulo

  Award: 2005/00587-5
• CN
  Conselho Nacional de Desenvolvimento Científico e Tecnológico

  Award: 300722/98-2