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.


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    Title :

    Adaptive feature enhancement of retinal vascular images using wavelet-based multiresolution analysis


    Contributors:
    Xu, Lei (author) / Zheng, Xiaoxiang (author) / Yu, Yajun (author) / Zhang, Hengyi (author)

    Conference:

    International Symposium on Multispectral Image Processing (ISMIP'98) ; 1998 ; Wuhan,China


    Published in:

    Publication date :

    1998-09-25





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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