Title: Contourlet transform and morphological reconstruction based retinal blood vessel segmentation
Authors: N. Sathya; K. Karuppasamy; P. Suresh
Addresses: Anna University Chennai, Tamil Nadu, India ' Department of Mechanical Engineering, Anna University Regional Campus - Tirunelveli, Tirunelveli, Tamil Nadu, India ' Department of Electronics and Communication, Vel Tech Dr. RR & SR R&D Institute of Science and Technology, Avadi, Chennai - 62, Tamil Nadu, India
Abstract: Retinal images are used in ocular fundus operations as well as human recognition. It has significant role in early detection of diabetic retinopathy, hypertensive retinopathy and retinitis pigmentosa by comparing the states of retinal blood vessels. The novel vessel segmentation method presented in this work starts with the contrast adjustment of green channel image representation to increase the dynamic range of the grey levels. A multi-scale method for retinal image contrast enhancement based on Contourlet transform is employed on the contrast adjusted image. The Contourlet transform provides better performance in enhancing the vessel-like segments than the Wavelets and Curvelets with its anisotropy and directionality characteristics, and is well-suited for multi-scale and multi-directional edge enhancement. Contourlet coefficients obtained via the contourlet transform in corresponding sub-bands are modified using a non-linear function. Noise is taken for more precise reconstruction and better visualisation. The proposed algorithm is best suited for fast processing applications.
Keywords: anisotropy and directionality; contourlet transform; multi-structure; elements morphology; morphological operators by reconstruction; retinal image; curvelets; false edges removal; length filtering; inverted green channel.
International Journal of Biomedical Engineering and Technology, 2017 Vol.25 No.2/3/4, pp.105 - 119
Available online: 23 Oct 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article