Adaptive Dictionary Learning Based Multimodal Branch Retinal Vein Occlusion Fusion

Abstract

Multimodal imaging can provide complementary information for branch retinal vein occlusions (BRVO) visualization. We proposed an adaptive dictionary learning based multimodal BRVO fusion method for color fundus photography (CFP), Fluorescein angiography (FA) and optical coherence tomography angiography (OCTA) images. First, the contrast of lesion areas in CFP and FA images was enhanced by using a local contrast enhancement algorithm based on standard deviation, and meanwhile a Frangi filter based algorithm was adopted to enhance vessels in OCTA images. Then, the local energy and multi-scale spatial frequency of image patches were calculated as brightness and gradient features respectively. The K-singular value decomposition algorithm was performed to train and generate the brightness and gradient sub-dictionaries, which were merged together to obtain the final adaptive dictionary. Finally, the orthogonal matching pursuit algorithm was adopted to calculate the sparse representation coefficient, and the maximum absolute value fusion strategy was applied to combine multimodal information. Experimental results demonstrate that the proposed method is more effective for BRVO visualization than the single-modality image, because our method can combine complementary information from multimodal images. It is useful for the clinical evaluation of BRVO.