Curvelet Transform based Retinal Image Analysis

Abstract

Edge detection is an important assignment in image processing, as it is used as a primary tool for pattern  recognition, image segmentation and scene analysis.  An edge detector is a high-pass filter that can be applied for extracting the edge points within an image. Edge detection in the spatial domain is  accomplished through convolution with a set of directional derivative masks in this domain. On the other hand, working in the  frequency domain has many advantages, starting from introducing an alternative description to the  spatial representation and providing more efficient and faster computational schemes with less sensitivity  to noise through high filtering, de-noising and compression algorithms. Fourier transforms, wavelet and  curvelet transform are among the most widely used frequency-domain edge detection from satellite  images. However, the Fourier transform is global and poorly adapted to local singularities. Some of  these draw backs are solved by the wavelet transforms especially for singularities detection and  computation. In this paper, the relatively new multi-resolution technique, curvelet transform, is assessed  and introduced to overcome the wavelet transform limitation in directionality and scaling.  In this research paper, the assessment of second generation curvelet transforms as an edge detection tool  will be introduced and compared with first generation cuevelet transform.

DOI:http://dx.doi.org/10.11591/ijece.v3i3.2451