The goal of this research is to put forward a high efficient method that can generate clinically useful images with improved visualization of retinal vascular image features. Methods: (1) Multiresolution decomposition of an original retinal image into subband images via 2D wavelet transformation (WT). (2) At S equals 21, the LLMMSE estimate algorithm that is applicable for nonstationary image model is used in wavelet domain to reduce noise. At s equals 22, soft thresholding wavelet shrinkage technique is used in wavelet domain to reduce noise. At s >= 23, semisoft wavelet shrinkage technique is applied in wavelet domain to further reduce noise. (3) Opening of a gray-scale Adaf subband image by a gray-scale circular structuring element can generate a background image. Enhancement is achieved by using this opened image as a unsharp mask and then applying morphological spatial filtering technique to enhance vessel contrast. (4) Reconstruction of retinal image from modified subband images via inverse 2-DWT. Experimental results show that the proposed approach is able to adaptively enhance subtle vascular features, suppress noise and improve global visualization of retinal vascular images.
Adaptive feature enhancement of retinal vascular images using wavelet-based multiresolution analysis
International Symposium on Multispectral Image Processing (ISMIP'98) ; 1998 ; Wuhan,China
Proc. SPIE ; 3545
1998-09-25
Conference paper
Electronic Resource
English
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