Use of the K-distribution for classification of breast masses.

The K-distribution had been introduced as a valid model to represent the statistics of the envelope of the backscattered echo from phantom and tissue. This paper investigates the efficacy of the parameters of this statistical model; namely, the effective number and the effective cross-section, to characterize breast lesions as benign or malignant. Based on the normalized values of the effective number and the effective scattering cross-section, images containing benign and malignant masses were classified for a data set from 52 patients having breast masses/lesions. The receiver operating characteristic (ROC) curves were then obtained to test the classification based on these two parameters. The results indicate that the parameters of the K-distribution may be useful in classification of the breast lesions as benign and malignant.

Using phase information in ultrasonic backscatter for in vivo liver analysis.

In recent studies, it has been shown that information about scatterer spacing can be obtained from analyzing the phase of the ultrasound echo from various media. Such information proves to be useful when examining the ultrasonic backscatter from well-organized tissue, such as the liver. By quantifying the deviations in scatterer spacing and/or varying degrees of regularity, conclusions may be drawn about the underlying pathology of the tissue. This paper examines the physical basis of how the scatterer locations affect the phase of the data. Computer simulations were performed that mimic various scattering conditions and that display the effects of differing degrees of regularity, as well as increases in a diffuse random background scattering component. Results of studies on a phantom are also included to investigate and display the phase response under well-controlled scattering conditions. Finally, in vivo data taken from liver scans were analyzed. In this work, it was shown that the phase of the backscattered signal holds valuable information regarding the pathological state of liver tissue. It is suggested that this simple examination of the phase can be refined into a technique to be used as a method to consistently detect the onset of pathological change.

Studies on the use of non-Rayleigh statistics for ultrasonic tissue characterization.

The research groups at Drexel University and Thomas Jefferson University had proposed the use of non-Rayleigh statistics for tissue characterization. Previous work based on the hypothesis that the envelope of the backscattered echosignal from abnormal regions of the tissue is more likely to be K-distributed than Rayleigh distributed, used the parameter of the K-distribution, M, to distinguish between regions containing benign or malignant masses and normal ones. In this work the B-scan breast images of 19 patients were studied using this approach. Previous studies have also been extended to exploit the existence of non-uniform phase characteristics of the echosignal from scatterers with some regular spacings, such as those in a periodic or quasi-periodic alignment. Computer simulations were carried out to show that the phase statistics deviate significantly from uniform in the range of (0, 2 pi) if the imaging region contained a number of periodically aligned (regular lattice) scatterers along with a collection of randomly distributed scatterers resulting in a quasi-periodic arrangement. This methodology was then applied to B-scan images of the breasts to distinguish between benign and malignant masses. If benign lesions show some sort of quasi-periodic or regular structures in the tissue, they will present non-uniform phase characteristics while more randomly structured malignant masses will have uniform phase characteristics. It is seen that the K-distribution may be used to identify the abnormal regions in the breast images and information on the phase may be used to further separate the abnormal regions into benign and malignant ones.

Classification of ultrasonic B mode images of the breast using frequency diversity and Nakagami statistics.

The parameters of the Nakagami distribution have been utilized in the past to classify lesions in breast tissue as benign or malignant. To avoid the effect of operatorgain settings on the parameters of the Nakagami distribution, normalized parameters were utilized for the classification. The normalized parameter was defined as the ratio of the parameter at the site of the lesion to its average value over several regions away from the site. This technique, however, was very time consuming. In this paper, the application of frequency diversity and compounding is explored to achieve this normalization. Lesions are classified using these normalized parameters at the site. A receiver operating characteristic (ROC) analysis of the parameters of the Nakagami distribution has been conducted before and after compounding on a data set of 60 benign and 65 malignant lesions. The ROC results indicate that this technique can reasonably classify lesions in breast tissue as benign or malignant.

Classification of ultrasonic B-mode images of breast masses using Nakagami distribution.

The Nakagami distribution was proposed recently for modeling the echo from tissue. In vivo breast data collected from patients with lesions were studied using this Nakagami model. Chi-square tests showed that the Nakagami distribution is a better fit to the envelope than the Rayleigh distribution. Two parameters, m (effective number) and alpha (effective cross section), associated with the Nakagami distribution were used for the classification of breast masses. Data from 52 patients with breast masses/lesions were used in the studies. Receiver operating characteristics (ROC) were calculated for the classification methods based on these two parameters. The results indicate that these parameters of the Nakagami distribution may be useful in classification of the breast abnormalities. The Nakagami distribution may be a reasonable means to characterize the backscattered echo from breast tissues toward a goal of an automated scheme for separating benign and malignant breast masses.

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.