A New Method For Extraction Of Image's Features: Complex Discrete Ripplet-II Transform

Abstract

Wavelet transform extracts the features of a signal and image via shifting and weighting methods. This transform has either advantages or disadvantages on image processing applications. One of important disadvantage of wavelet transform is limited orientation problem. This problem has been solved by different orientation with ridgelet transform. Ripplet-II transform is defined by recently generalising of the ridgelet transform by adding parameter degree (d). Complex discrete form of ripplet-II transform defined by this study and added to the literature. Also, complex discrete Ripplet-II transform was tested on medical image classification application. For this, the most common benign lesions in liver MR are classified as cyst and hemangioma using complex discrete Ripplet-II transform and ANN. Obtained results shown that, classification success of complex discrete Ripplet-II transform with complex-valued coefficients is higher than real-valued coefficients. © 2016 IEEE.