Retinal Vessel Segmentation Based on Feature Fusion and Spatially Constrained Co-Clustering

Retinal vessel segmentation plays a critical role in the early diagnosis of ocular and systemic diseases such as diabetic retinopathy and hypertension. This paper proposes an unsupervised segmentation method that integrates dual-feature fusion and spatially constrained co-clustering. The approach combines multi-scale and multi-directional vascular structural information captured by B-COSFIRE filters with local contrast enhancement from the Top-hat transformation, resulting in a complementary feature representation. Furthermore, a spatial constraint based on Markov Random Fields is incorporated into an information-theoretic co-clustering framework to improve topological consistency. Experimental results on the STARE database demonstrate that the proposed method achieves competitive performance, with an accuracy of 0.9511, sensitivity of 0.7439, and specificity of 0.9747, effectively enhancing vascular continuity and robustness.