Double-density Dual-tree Discrete Wavelet Transform Research Articles

Recaptured attack-resilient watermarking scheme

We propose a recaptured attack-resilient blind watermarking scheme. The work aims to recover the watermark information from the stego (watermarked image) reacquired through a digital liquid-crystal display monitor. In the age of digital globalization, problems like copyright and integrity infringement of digital content are growing exponentially. The content owner can prove their ownership by extracting the embedded watermark from the digitally breached data. The presented framework is robust to several geometrical and signal processing attacks, including lens distortions, intensity distortion, and moiré distortion caused by recapture attack. The singular values of a watermark image and a unique security key are embedded in the host image’s double-density dual-tree discrete wavelet transform (DDD-DWT) coefficients. However, most singular value decomposition (SVD) based watermarking algorithms are prone to false alarm rates and missed alarm rates, causing a security issue. On that account, the proposed watermarking scheme in this paper introduces a unique security key embedding for authentication at the decoder side. The experimental results show that the proposed framework provides high perceptibility with a WPSNR value >59 dB and robustness against various attacks and security.

Automated technique for coronary artery disease characterization and classification using DD-DTDWT in ultrasound images

Abstract Heart is one of the important as well as hardest working organ of human body. Cardiac ischemia is the condition where sufficient blood and oxygen will not reach the heart muscle due to narrowed arteries of the heart. This condition is called coronary artery disease. Several non-invasive diagnostic tests fail to reveal exact impact of coronary artery disease on myocardial segments. The ultrasound images can explore major impact on ventricular muscle segments due to ischemia and complication of acute coronary syndrome. Computer aided diagnosis tools can predict coronary artery disease in its early stage so that patients can undergo treatment and save their life. This paper presents a novel computer aided diagnosis system for the automated detection of coronary artery disease using echocardiography images taken from four chamber heart. Proposed method uses double density-dual tree discrete wavelet transform (DD-DTDWT) to decompose the images into different frequency sub-bands. Then various entropy features are extracted from these sub-bands. The obtained dimension of the features is reduced using marginal fisher analysis (MFA) and optimal features are selected using feature ranking methods. The proposed method achieved promising accuracy of 96.05%, sensitivity of 96.12%, and specificity of 96.00% for linear discriminant classifier using entropy ranking method with twelve features. We have also proposed coronary artery disease risk index (CADRI) to categorize diseased subjects from normal subjects using a single value. Thus, it can be used as a diagnosis tool in hospitals and polyclinics for confirming the findings of clinicians.

The Double-Density Dual-Tree DWT

This paper introduces the double-density dual-tree discrete wavelet transform (DWT), which is a DWT that combines the double-density DWT and the dual-tree DWT, each of which has its own characteristics and advantages. The transform corresponds to a new family of dyadic wavelet tight frames based on two scaling functions and four distinct wavelets. One pair of the four wavelets are designed to be offset from the other pair of wavelets so that the integer translates of one wavelet pair fall midway between the integer translates of the other pair. Simultaneously, one pair of wavelets are designed to be approximate Hilbert transforms of the other pair of wavelets so that two complex (approximately analytic) wavelets can be formed. Therefore, they can be used to implement complex and directional wavelet transforms. The paper develops a design procedure to obtain finite impulse response (FIR) filters that satisfy the numerous constraints imposed. This design procedure employs a fractional-delay allpass filter, spectral factorization, and filterbank completion. The solutions have vanishing moments, compact support, a high degree of smoothness, and are nearly shift-invariant.