Purpose: Benign and malignant tumors can be classified by using texture analysis of the ultrasound B-scan image to describe the variation in the echogenicity of scatterers. The recently proposed ultrasonic Nakagami parametric image has also been used to detect the concentrations and arrangements of scatterers for tumor characterization applications. B-scan-based texture analysis and the Nakagami parametric image are functionally complementary in ultrasonic tissue characterizations and this study aimed to combine these methods in order to improve the ability to characterize breast tumors. Methods: To validate this concept, radio-frequency data obtained from 130 clinical cases were used to construct the texture-feature parametric image and the Nakagami parametric image. Four texturefeature parameters based on a gray-level co-occurrence matrix ͑homogeneity, contrast, energy, and variance͒ and the Nakagami parameters of the benign and malignant tumors were calculated. The usefulness of an individual parameter was determined and scatter graphs indicated the relationship between two selected texture-feature parameters. Fisher's linear discriminant analysis was used to combine the selected texture-feature parameters with the Nakagami parameter. The performance in classifying tumors was evaluated based on the receiver operating characteristic curve. Results: The results indicated that there is a trade-off between sensitivity and specificity when using an individual texture-feature parameter or when combining two such correlated parameters to discriminate benign and malignant cases. However, the best performance was obtained when combining selected texture-feature parameters with the Nakagami parameter. Conclusions:The study findings suggest that combining B-scan-based texture analysis and the Nakagami parametric image could improve the ability to classify benign and malignant breast tumors.

These findings indicate that the strain-compounding Nakagami imaging method based on the acquisition of multiple frames under different strain states could provide objective information that would improve the ability to classify benign and malignant breast tumors.

No abstract

Texture analysis of breast ultrasound B-scans has been widely applied to the segmentation and classification of breast tumors. We present a parametric imaging method based on the texture features to preserve tumor edges and retain the texture information simultaneously. Four texture-feature parameters--homogeneity, contrast, energy and variance--were evaluated using the gray-level co-occurrence matrix. The local texture-feature parameter was assigned as the new pixel located at the center of the sliding window at each position. This process yielded the texture-feature parametric image as the map of texture-feature values. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were estimated to show the quality improvement of the images. The contours outlined from 11 experienced physicians and the gradient vector flow (GVF) snake algorithm segmentations were adopted to verify the edge enhancement of texture-feature parametric images. In addition, the Fisher's linear discriminant analysis (FLDA) and receiver-operating-characteristic (ROC) curve were used to test the performance of breast tumor classifications between texture-feature parametric images and B-scan images. The results show that the variance images have higher CNR and SNR estimates than those in the B-scan images. There was a high agreement between the physician's manual contours and the GVF snake automatic segmentations in the variance images, and the mean area overlap was over 93%. The area under the ROC curve from the B-scan images had 0.81 and 95% confidence interval of 0.72-0.88, and the texture-feature parametric images had 0.90 and 95% confidence interval of 0.84-0.96. These findings indicate that the texture-feature parametric imaging method can be not only useful for determining the location of the lesion boundary but also as a tool to improve the accuracy of breast tumor classifications.

The usefulness of breast ultrasound could be extended by improving the detection of microcalcifications by being able to detect and enhance microcalcifications while simultaneously eliminating hyperechoic spots (e.g., speckle noise and fibrocystic changes) that can be mistaken for microcalcifications (i.e., false microcalcifications). This study investigated the use of a strain-compounding technique with speckle factor (SF) imaging to analyze the degree of scatterer redistributions in breast tissues under strain conditions for identifying microcalcifications and false microcalcifications. The efficacy of the proposed method was tested by collecting raw data of ultrasound backscattered signals from 26 lesions at BI-RADS category 4 or 5 with suspicious microcalcifications. The different strain conditions were created by applying manual compression to deform the breast lesion. For each region in which microcalcifications were suspected, estimates of the SNR of the strain-compounding B-scan images and estimates of the mean SF (SFavg) in the strain-compounding SF images were calculated. Compared with microcalcifications, the severity of speckle of the false microcalcifications would be easily degraded under compressive strain conditions. The results demonstrated that the SNR estimates in the strain-compounding B-scan images for microcalcifications and false microcalcifications were 5.22 ± 1.04 (mean ± standard deviation) and 4.62 ± 1.09, respectively; the corresponding SFavg estimates in the strain-compounding SF images were 0.47 ± 0.10 and 0.22 ± 0.10 (p < 0.01). The mean area under the receiver operating characteristic curve using the SNR estimate was 0.71, whereas that using the SFavg estimate was 0.94. These findings indicate that the strain-compounding SF imaging method is more effective at discriminating between microcalcifications and false microcalcifications.