Application of the compound probability density function for characterization of breast masses in ultrasound B scans.

The compound probability density function (pdf) is investigated for the ability of its parameters to classify masses in ultrasonic B scan breast images. Results of 198 images (29 malignant and 70 benign cases and two images per case) are reported and compared to the classification performance reported by us earlier in this journal. A new parameter, the speckle factor, calculated from the parameters of the compound pdf was explored to separate benign and malignant masses. The receiver operating characteristic curve for the parameter resulted in an A(z) value of 0.852. This parameter was combined with one of the parameters from our previous work, namely the ratio of the K distribution parameter at the site and away from the site. This combined parameter resulted in an A(z) value of 0.955. In conclusion, the parameters of the K distribution and the compound pdf may be useful in the classification of breast masses. These parameters can be calculated in an automated fashion. It should be possible to combine the results of the ultrasonic image analysis with those of traditional mammography, thereby increasing the accuracy of breast cancer diagnosis.

Classification of breast masses in ultrasonic B-mode images using a compounding technique in the Nakagami distribution domain.

Classification of masses in ultrasonic B-mode images of the breast tissue using "normalized" parameters of the Nakagami distribution was recently investigated. The technique, however, did not yield performances that were comparable to those of an experienced radiologist, and utilized only a single image for tissue characterization. Because radiologists commonly use two to four images of a mass for characterization, a similar procedure is developed here. A simple summation of the normalized Nakagami parameters from two different images of a mass is utilized for classification as benign or malignant. The performance of the normalized Nakagami parameters before and after the summation has been carried out through a receiver operating characteristic (ROC) study. The bootstrap procedure has been utilized to compute the mean and SD of the ROC area, A(z), obtained for each parameter. It has been observed that combining normalized Nakagami parameters from two images of the mass may help to improve classification performance over that from utilizing the parameters of just a single image. The performance of this automated parameter-based approach appears to match that of a trained radiologist.

Computer aided classification of masses in ultrasonic mammography.

Frequency compounding was recently investigated for computer aided classification of masses in ultrasonic B-mode images as benign or malignant. The classification was performed using the normalized parameters of the Nakagami distribution at a single region of interest at the site of the mass. A combination of normalized Nakagami parameters from two different images of a mass was undertaken to improve the performance of classification. Receiver operating characteristic (ROC) analysis showed that such an approach resulted in an area of 0.83 under the ROC curve. The aim of the work described in this paper is to see whether a feature describing the characteristic of the boundary can be extracted and combined with the Nakagami parameter to further improve the performance of classification. The combination of the features has been performed using a weighted summation. Results indicate a 10% improvement in specificity at a sensitivity of 96% after combining the information at the site and at the boundary. Moreover, the technique requires minimal clinical intervention and has a performance that reaches that of the trained radiologist. It is hence suggested that this technique may be utilized in practice to characterize breast masses.

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.

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.

Imaging of the patient with silicone gel breast implants.

There are two reasons for radiologic evaluation of the augmented breast. Because women with implants are at the same risk for breast cancer as other women, imaging is performed to screen for cancer or to work up clinical abnormalities. Additionally, imaging allows assessment of implant integrity. The various methods for imaging implants and breast tissue in the augmented patient are discussed. Imaging findings suggestive of silicone gel implant rupture are presented.

On modeling biomedical ultrasound RF echoes using a power-law shot-noise model.

We propose a new model for the RF ultrasound echo, namely the power-law shot-noise process. Based on this model, the in-phase and quadrature components of the echo are shown to exhibit 1/f beta-type spectral behavior, in a sense that is defined in the paper. The envelope also exhibits this type of spectral behavior, but with a different exponent. This result explains the experimental observations by other researchers of the power-law trend of the RF echo spectrum. Although the shot-noise model has been used in the past for modeling the RF echo, this is the first time that a power-law impulse response filter is used and that the resulting 1/f beta-type spectral behavior of the RF echo has been investigated. The model parameters are linked to tissue characteristics, such as scatterer density and attenuation; thus, they have the potential to be used as tissue characterization features. The validity of the proposed model is tested based on a database of 100 clinical ultrasound images of the breast.

Tissue characterization using the continuous wavelet transform. Part II: Application on breast RF data.

In the first part of this work [16], a wavelet-based decomposition algorithm of the RF echo into its coherent and diffuse components was introduced. In this paper, the proposed algorithm is used to estimate structural parameters of the breast tissue such as the number and energy of coherent scatterers, the energy of the diffuse scatterers, and the correlation between them. Based on these individual parameters, breast tissue characterization is performed. The database used consists of 155 breast scans from 42 patients. The results are presented in terms of empirical receiver operating characteristics (ROC) curves. The results of this study are discussed in relation to the tissue microstructure. Individual estimated parameters are able to differentiate reliably between normal and fibroadenoma or fibrocystic or cancerous tissue (area under the ROC Az > 0.93). Also, the differentiation between malignant and benign (normal, fibrocystic, and fibroadenoma) tissue was possible (Az > 0.89).

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.

Displacement of microcalcifications during stereotactic 11-gauge directional vacuum-assisted biopsy with marking clip placement: case report.

A 53-year-old woman with right breast microcalcifications of intermediate concern underwent stereotactic directional vacuum-assisted biopsy with marking clip placement. Postbiopsy mammograms showed displacement of a few of the targeted microcalcifications adjacent to misplaced marker clips. Mammography following stereotactic breast biopsy is important to document the location and number of residual calcifications and to determine the adequacy and location of clip placement.

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.

Developing asymmetric breast tissue.

PURPOSE: To show that benign asymmetric breast tissue detected mammographically may increase over time. MATERIALS AND METHODS: Serial mammograms obtained in 21 women with negative physical examination results and mammographically detected developing asymmetric breast tissue were reviewed, and findings were correlated with results of biopsy (n = 16), ultrasonography (US) (n = 8), and contrast material-enhanced magnetic resonance (MR) imaging (n = 3). Five patients who did not undergo biopsy were followed up for 13-84 months. Thirteen of 16 biopsy specimens were reviewed. RESULTS: At the time of mammographic change, 12 patients without baseline asymmetric tissue had a mean age of 41.7 years and a mean size of asymmetric tissue of 2.4 cm. The mean age of nine patients with baseline asymmetric tissue was 46.9 years. In eight patients, the mean size increase was 2.5 cm. One patient showed increased tissue density but stable size. All US and MR images were negative. Pseudoangiomatous stromal hyperplasia was present in all 13 biopsy specimens reviewed and extensive in 12. No malignancies have been reported in five of the followed-up patients, and two have had continued enlargement of asymmetric tissue. CONCLUSION: Pseudoangiomatous stromal hyperplasia is a common histopathologic finding in developing asymmetric breast tissue. Follow-up, rather than biopsy, is a management option if benign imaging and clinical criteria are met.

Comparisons of the Rayleigh and K-distribution models using in vivo breast and liver tissue.

There is a strong interest in finding out which statistical model is the most appropriate for describing the envelope of the backscattered ultrasonic echoes from different types of tissues. The Rayleigh model is commonly employed, but this requires conditions, such as the presence of large number of randomly located scatterers with fairly uniform cross-sections, that are not always met. However, our research indicates that a model based on the K-distribution may provide a better fit to empirical data over a range of scattering conditions than the standard Rayleigh model. In this study, we looked at the K-distribution as a descriptor of the backscattered envelope of the breast and liver tissues (in vivo). By examining data from various tissue regions, a goodness-of-fit test (a least squares error method) was used to determine whether a Rayleigh or K-distribution model is more appropriate. From a large group of patients and volunteer scans (a total of 72 subjects), the fit between the K-distribution and the data is shown to have a much smaller error than the Rayleigh model.

MRI of benign and malignant hepatic lobar atrophy.

We assessed the magnetic resonance imaging (MRI) features of hepatic lobar atrophy. Two of us reviewed MRIs of the liver in eight patients with benign or malignant forms of lobar atrophy. All atrophic lobes showed low signal intensity on T1-weighted images and high signal intensity on T2-weighted images compared with the remainder of the liver, and all showed greater enhancement compared to the nonatrophic lobe. Atrophic lobes have suggestive MRI findings and are similar for both benign and malignant etiologies.

Contrast-enhanced breast MRI: factors affecting sensitivity and specificity.

Contrast-enhanced MRI (CE-MRI) of the breast has been investigated for over 10 years. The reports of sensitivity for cancer detection have generally been greater than 90 %. However, estimates of specificity have varied greatly. Differing results are due to differences in study populations, technical methods and criteria for interpretation. Early and marked signal rise, detected using dynamic imaging technique following contrast administration, is the MRI hallmark of cancer. However, some malignant lesions may enhance slowly or minimally, and a variety of benign lesions may enhance rapidly with marked signal intensity. High resolution techniques generally requiring longer acquisition times are more likely to depict the slowly enhancing malignancies at the cost of a decrease in specificity due to lack of temporal resolution. This disadvantage may be offset by the improved visualization of lesion morphology with high resolution images. This report reviews the methods and results of the leading investigators of breast MRI.

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.

Breast MR imaging for cancer detection and implant evaluation: potential pitfalls.

Magnetic resonance (MR) imaging of the breast is currently used for evaluation of both parenchymal disease and silicone gel implants. MR imaging has the potential to address questions raised or unanswered with traditional diagnostic imaging methods. However, lesion specificity and cancer sensitivity depend on multiple technical factors (i.e., imaging parameters and contrast agent delivery) and biologic factors (i.e., the menstrual status of the patient and the vascularity of the lesion). Although diagnostic criteria for parenchymal disease have been reported, overlap of malignant and benign enhancement profiles occurs. The accuracy of implant evaluation depends on the imaging parameters and knowledge of the implant type and surgical history. In addition, individual investigative imaging methods are diverse, causing difficulty in protocol development for the practicing radiologist. An awareness of the problematic factors in breast MR imaging will improve diagnostic accuracy and allow understanding of the limitations of the modality and individual patient examinations.