Name
Title | ||
|---|---|---|
Frequency decomposition and compounding of ultrasound medical images with wavelet packets |
Abstract | ||
|---|---|---|
Ultrasound beams propagating in biological tissues undergo distortions due to local inhomogeneities of the acoustic parameters and the nonlinearity of the medium. The spectral analysis of the radio-frequency (RF) backscattered signals may yield important clinical information in the field of tissue characterization, as well as enhancing the detectability of tissue parenchymal diseases. Here, the authors propose a new tissue spectral imaging technique based on the wavelet packets (WP) decomposition. In a conventional ultrasound imaging system, the received echo-signals are generally decimated to generate a medical image, with a loss of information. With the proposed approach, all the RF data are processed to generate a set of frequency subband images. The ultrasound echo signals are simultaneously frequency decomposed and decimated, by using two quadrature mirror filters, followed by a dyadic subsampling. In addition, to enhance the lesion detectability and the image quality, the authors apply a nonlinear filter to reduce noise in each subband image. The proposed method requires simple additional signal processing and it can be implemented on any real-time imaging system. The frequency subband images, which are available simultaneously, can be either used in a multispectral display or summed up together to reduce speckle noise. To localize the different frequency response in the tissues, the authors propose a multifrequency display method where 3 different subband images, chosen among those available, are encoded as red, green, and blue intensities (RGB) to create a false-colored RGB image. According to the clinical application, different choices can evidence different spectral proprieties in the biological tissue under investigation. To enhance the lesion contrast in a grey-level image, one of the possible methods is the summation of the images obtained from narrow frequency subbands, according to the frequency compounding technique. The authors show that by adding t- - he denoised subband images created with the WP decomposition, the contrast-to-noise ratio in 2 phantom images is largely increased. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/42.938244 | Medical Imaging, IEEE Transactions |
Keywords | Field | DocType |
acoustic signal processing,backscatter,biomedical ultrasonics,image enhancement,medical image processing,ultrasonic propagation,wavelet transforms,beam distortions,biological tissues,contrast-to-noise ratio,dyadic subsampling,frequency decomposition,frequency subband images set generation,image quality,images summation,lesion contrast enhancement,lesion detectability,medical diagnostic imaging,medium nonlinearity,noise reduction,quadrature mirror filters,radio-frequency backscattered signals spectral analysis,received echo-signals,ultrasound medical images compounding,wavelet packets | Computer vision,Spectral imaging,Frequency response,Imaging phantom,Signal-to-noise ratio,Multispectral image,Image quality,Artificial intelligence,Speckle noise,Wavelet packet decomposition,Mathematics | Journal |
Volume | Issue | ISSN |
20 | 8 | 0278-0062 |
Citations | PageRank | References |
29 | 3.88 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriella Cincotti | 1 | 34 | 14.04 |
| Giovanna Loi | 2 | 29 | 3.88 |
| Massimo Pappalardo | 3 | 129 | 18.23 |