Analysis of microstructural alterations of normal and pathological breast tissue in vivo using the AR cepstrum

Alignment of an acoustic manipulation device with cepstral analysis of electronic impedance data

“…Cepstral analysis is well established as a signal processing method for removing echoes in speech recognition software [6], and of more relevance, there have been reports into its use for breast cancer detection from ultrasound images [8]. Such techniques use a similar interpretation of the cepstrum as presented here.…”

Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties

“…The SAS parameter has proven to be effective for characterizing diffuse liver disease 17-20 and breast cancer. 21 Several methods have been used to estimate SAS, including the cepstrum, 22 wavelet transform, 23,24 and spectral autocorrelation (SAC) methods. 25 All methods involve the detection of the periodicity of a power spectrum, from which the spacing between quasiregular scatterers can be determined.…”

Histology-based simulations for the ultrasonic detection of microscopic cancer in vivo

“…Extensive experimental data show tissue modifications due to breast, prostate, and other cancers affect ultrasonic backscatter measurements, and both empirical and analytical models have been developed to estimate tissue parameters such as cell sizes and distributions. [2][3][4][5][6][7] Empirical models statistically define spectral parameters from tissue measurements, and studies have shown excellent agreement for homogeneous tissues but discrepancies for heterogeneous tissues. 7 Analytical models average the scattering from uniformly distributed cells or cell nuclei, and employ simplifications such as single scattering, weak scattering (Born approximation), and fluidlike tissue properties (no shear waves).…”