关键词: 视网膜血管分割, 特征组合, 多模块学习, [k]-means

Abstract: Machine learning requires a manually annotated set of training images for classifying a pixel either as a vessel or a non-vessel in previously unseen image. It’s difficult to obtain the training samples and expensive time. A new unsupervised learning approach based on feature fusion is proposed. Firstly, a set of 18-D discriminative feature vectors, consisting of Hu moment invariants, Hessian, phase congruency, Gabor wavelet transform, Candy edge detector, are extracted for each pixel of the fundus image. Then a matrix based on the feature vectors is divided into multimodel sets, and uses the [k]-means method to cluster respectively. Finally, the clustering?results are combined as output of the retinal vessel segmentation. Experimental results show that the proposed approach has good average accuracy and running time.

Key words: retina vessel segmentation, feature combination, multimodel learning, [k]-means