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To use feed-forward active contours (snakes) to track and measure brachial artery vasomotion on ultrasound images recorded in both transverse and longitudinal views; and to compare the algorithm's performance in each view.Longitudinal and transverse view ultrasound image sequences of 45 brachial arteries were segmented by feed-forward active contour (FFAC). The segmented regions were used to measure vasomotion artery diameter, cross-sectional area, and distention both as peak-to-peak diameter and as area. ECG waveforms were also simultaneously extracted frame-by-frame by thresholding a running finite-difference image between consecutive images. The arterial and ECG waveforms were compared as they traced each phase of the cardiac cycle.FFAC successfully segmented arteries in longitudinal and transverse views in all 45 cases. The automated analysis took significantly less time than manual tracing, but produced superior, well-behaved arterial waveforms. Automated arterial measurements also had lower interobserver variability as measured by correlation, difference in mean values, and coefficient of variation. Although FFAC successfully segmented both the longitudinal and transverse images, transverse measurements were less variable. The cross-sectional area computed from the longitudinal images was 27% lower than the area measured from transverse images, possibly due to the compression of the artery along the image depth by transducer pressure.FFAC is a robust and sensitive vasomotion segmentation algorithm in both transverse and longitudinal views. Transverse imaging may offer advantages over longitudinal imaging: transverse measurements are more consistent, possibly because the method is less sensitive to variations in transducer pressure during imaging.

Purpose: To use feed-forward active contours (snakes) to track and measure brachial artery vasomotion on ultrasound images recorded in both transverse and longitudinal views; and to compare the algorithm's performance in each view. Methods: Longitudinal and transverse view ultrasound image sequences of 45 brachial arteries were segmented by feed-forward active contour (FFAC). The segmented regions were used to measure vasomotion artery diameter, cross-sectional area, and distention both as peak-to-peak diameter and as area. ECG waveforms were also simultaneously extracted frame-by-frame by thresholding a running finite-difference image between consecutive images. The arterial and ECG waveforms were compared as they traced each phase of the cardiac cycle. Results: FFAC successfully segmented arteries in longitudinal and transverse views in all 45 cases. The automated analysis took significantly less time than manual tracing, but produced superior, well-behaved arterial waveforms. Automated arterial measurements also had lower interobserver variability as measured by correlation, difference in mean values, and coefficient of variation. Although FFAC successfully segmented both the longitudinal and transverse images, transverse measurements were less variable. The cross-sectional area computed from the longitudinal images was 27% lower than the area measured from transverse images, possibly due to the compression of the artery along the image depthmore » by transducer pressure. Conclusions: FFAC is a robust and sensitive vasomotion segmentation algorithm in both transverse and longitudinal views. Transverse imaging may offer advantages over longitudinal imaging: transverse measurements are more consistent, possibly because the method is less sensitive to variations in transducer pressure during imaging.« less

Purpose: The overall goal of this study is to develop a computer‐based image analysis system for breast ultrasound that may aid physicians in differentiating benign and malignant masses with higher confidence and thus help in reducing unnecessary biopsies. Towards this goal we describe an approach that combines two independent probabilistic classifiers to improve diagnosis of breast masses. Methods: B‐scan images of 266 patients with biopsy proven breast masses were analyzed for margin grayscale and shape features. These features were used with two statistical methods, logistic regression and naive Bayes, to classify the lesions as malignant and benign. The diagnostic performance of the ultrasound image features was evaluated by using them alone and by combining them with mammographic BI‐RADS categories and patient age. The probability predictions of the two classifiers were compared to assess consensus between them. The performances of the classifiers were evaluated using area under the curve (AUC) of the ROC Receiver Operating Characteristic. Training and testing were performed using leave‐one‐out validation. Results: The results showed that combined features outperformed the individual features. Logistic regression performed slightly better than naive Bayes (AUC: 0.902 ± 0.023 vs. 0.865 ± 0.027, p < 0.03). The agreement between the two models at different decision thresholds was stable at ∼88 %. The reaming 12% were treated as the cases that needed further diagnostic investigation. Treating each algorithm as an independent observer and using the consensus between the two models as the criterion for mass differentiation demonstrated a reduction in biopsy by 48% could be achieved at the cost of missed malignancies in 2% cases. Conclusion: Computer‐based image analysis of breast ultrasound images can aid the differentiation of benign and malignant masses and help in improving biopsy yields. NIH grant R01 CA130946