RETINAL VESSEL ENHANCEMENT USING MULTI-DICTIONARY AND SPARSE CODING

Bin Chen (1, 2) , Yang Chen (1, 2, 3) , Zhuhong Shao (4) , Limin Luo (1, 2)

1 CRIBS - Centre de Recherche en Information Biomédicale sino-français
2 LIST - Laboratory of Image Science and Technology [Nanjing]
3 LTSI - Laboratoire Traitement du Signal et de l'Image
4 Capital Normal University [Beijing]

A novel retinal vessel enhancement method based on multi dictionary and sparse coding is proposed in this paper. Two dictionaries are utilized to gain the retinal vascular structures and details, one is the representation dictionary (RD) generated from the original retinal images, and another is the enhancement dictionary (ED) extracted from the corresponding label images. The proposed method represents the input image with RD to get the sparse coefficients via a sparse coding process. Then the enhanced retinal vessel image is obtained from the solved sparse coefficients and ED. Experimental results performed on the DRIVE and STARE databases indicate that the proposed method not only can effectively improve the image contrast but also enhance the details of the retinal vessels.

Mots clés: image-enhancement; segmentation; representations; algorithm

Domaines: Ingénierie biomédicale

Bin Chen, Yang Chen, Zhuhong Shao, Limin Luo. RETINAL VESSEL ENHANCEMENT USING MULTI-DICTIONARY AND SPARSE CODING. IEEE International Conference on Acoustics, Speech, and Signal Processing, Mar 2016, Shanghai, China. pp.893--897. ⟨hal-01438973⟩