Biomedical subjects

K D Donohue

Publications and source records attributed to K D Donohue.

5 recordsLinked to original sources

PubMed2001

Tissue classification with generalized spectrum parameters.

This paper presents performance comparisons between breast tumor classifiers based on parameters from a conventional texture analysis (CTA) and the generalized spectrum (GS). The computations of GS-based parameters from radiofrequency (RF) ultrasonic scans and their relationship to underlying scatterer properties are described. Clinical experiments demonstrate classifier performances using 22 benign and 24 malignant breast mass regions taken from 40 patients. Linear classifiers based on parameters from the front edge, back edge and interior tumor regions are examined. Results show significantly better performances for GS-based classifiers, with improvements in empirical receiver operating characteristic (ROC) areas of greater than 10%. The ROC curves show GS-based classifiers achieving a 90% sensitivity level at 50% specificity when applied to the back-edge tumor regions, an 80% sensitivity level at 65% specificity when applied to the front-edge tumor regions, and a 100% sensitivity level at 45% specificity when applied to the interior tumor regions.

Breast Neoplasms↗

PubMed2000

A comparative study of transforms for use in digital audio data compression.

This paper provides a subjective quality analysis of transforms used in audio compression algorithms for a class of music signals. A 34-subject listener test compares three transforms in conjunction with an MPEG I layer 1 compression scheme. One test compares the performances of the discrete wavelet packet transform (DWPT) and the modified discrete cosine transform (MDCT) used in MPEG. Another test compares the performances of a DWPT eight-level nonuniform critical-band split and a DWPT five-level uniform subband split. Results indicate that the critical-band split provides significantly better quality than the uniform subband split for sounds with tonal and strong low-frequency content, while the DWPT outperforms the MDCT with significant improvement for nontonal sounds.

Algorithms↗

PubMed2000

Duct detection and wall spacing estimation in breast tissue.

The relationship between duct tissue and several types of malignant disease suggests that methods for characterizing duct structures may be useful tools in ultrasonic tissue characterization. This paper presents performance results from ultrasonic phantom experiments and Monte Carlo simulations for detecting and estimating duct wall spacings on the order of those typically found in breast tissue using methods based on the generalized spectrum (GS) and cepstrum. A performance comparison demonstrates the advantages of each method and examines the effects of various signal processing options, including a special normalization technique for the GS that effectively whitens the data spectrum and reduces interfering spectral influences with little overall performance loss. Experimental results (for both simulation and phantom) indicate that the GS typically achieves detection rates of over 90% (at 10% false alarm rates) over a broad range of SNR values (3-21 dB). The GS detection performance exceeds that of the cepstrum and exhibits more robustness to noise and signal processing parameters. Simulation results with fixed system effects indicate better estimation performance for cepstral-based methods, while experimental phantom results show the GS estimation performance to be the same or better than the cepstral-based method.

Algorithms↗

PubMed1994

Mean-scatterer spacing estimates with spectral correlation.

An ultrasonic backscattered signal from material comprised of quasiperiodic scatterers exhibit redundancy over both its phase and magnitude spectra. This paper addresses the problem of estimating mean-scatterer spacing from the backscattered ultrasound signal using spectral redundancy characterized by the spectral autocorrelation (SAC) function. Mean-scatterer spacing estimates are compared for techniques that use the cepstrum and the SAC function. A -scan models consist of a collection of regular scatterers with Gamma distributed spacings embedded in diffuse scatterers with uniform distributed spacings. The model accounts for attenuation by convolving the frequency dependent scattering centers with a time-varying system response. Simulation results indicate that SAC-based estimates converge more reliably over smaller amounts of data than cepstrum-based estimates. A major reason for the performance advantage is the use of phase information by the SAC function, while the cepstrum uses a phaseless power spectral density that is directly affected by the system response and the presence of diffuse scattering (speckle). An example of estimating the mean-scatterer spacing in liver tissue also is presented.

Culture Techniques↗

PubMed1993

Characterization of tissue microstructure scatterer distribution with spectral correlation.

Characterization of tissue microstructure from the backscattered ultrasound signal using the spectral autocorrelation (SAC) function provides information about the scatterer distribution in biological tissue. This paper demonstrates SAC capabilities in characterizing periodicities in A-scans due to regularity in the scatterer distribution. The A-scan is modelled as a cyclostationary signal, where the statistical parameters of the signal vary in time with single or multiple periodicities. This periodicity manifests itself as spectral peaks both in the power spectral density (PSD) and in the SAC. Periodicity in the PSD will produce a well defined dominant peak in the cepstrum, which has been used to determine the scatterer spacing. The relationship between the scatterer spacing and the spacing of the spectral peaks is established using a stochastic model of the echo-formation process from biological tissue. The distribution of the scatterers within the microstructure is modelled using a Gamma function, which offers a flexible method of simulating parametric regularity in the scatterer spacing. Simulations of the tissue microstructure for lower orders of regularity indicate that the SAC components reveal information about the scatterer spacing that are not seen in the PSD and the cepstrum. The echoformation process is tested by simulating microstructure of varying regularity and analyzing their effect on the SAC, PSD and cepstrum. Experimental validation of the simulation results are provided using in vivo scans of the breast and liver tissue that show the presence of significant spectral correlation components in the SAC.

Computer Simulation↗