Discrete wavelet transform vs. wavelet packets for texture analysis of ultrasound images of carotid atherosclerosis

In this paper, a scale/frequency approach, based on the wavelet transform, was used in an attempt to characterize carotid atherosclerotic plaque from B-mode ultrasound. Two wavelet decomposition schemes, namely the discrete wavelet transform (DWT) and wavelet packets (WP), and three basis functions, namely Haar, symlet3 and biorthogonal3.1, were investigated in terms of their ability to discriminate between symptomatic and asymptomatic cases. A total of 12 detail sub-images were extracted using the DWT and 255 using the WP decomposition schemes. It was shown that WP analysis by the use of Haar filter and the l-1 norm as texture descriptor could reveal differences not only in high but also in low frequencies, and therefore characterize efficiently the atheromatous tissue. Additional studies applying and further extending the above methodology are required to ensure the usefulness of wavelet-based texture analysis of carotid atherosclerosis.