Phase response is an essential parameter of vibration sensors in primary vibration calibration, heterodyne interferometer was usually used to obtain this parameter. To reduce sampling rate of heterodyne interferometer signal and sampling number, analog mixer and lowpass filter are used to down-convert the interferometer signal. However, the using of analog devices will introduce additional phase delay for the measurement of the phase response, it leads to the measurement by the calibration is unreliable. A novel correction of phase delay method was proposed to precisely measure the phase response of the vibration sensors. Experiment results show the performance of the proposed correction method.
In computer aided diagnosis for ultrasound images, breast lesion segmentation is an important but intractable procedure. Although active contour models with level set energy function have been proposed for breast ul- trasound lesion segmentation, those models usually select and x the weight values for each component of the level set energy function empirically. The xed weights might a ect the segmentation performance since the characteristics and patterns of tissue and tumor di er between patients. Besides, there is observer variability in probe handling and ultrasound machine gain setting. Hence, we propose an active contour model with adaptive parameters in breast ultrasound lesion segmentation to overcome the variability of tissue and tumor patterns between patients. The main idea is to estimate the optimal parameter set automatically for di erent input images. We used regression models using 27 numerical features from the input image and an initial seed box. Our method showed better results in segmentation performance than the original model with xed parameters. In addition, it could facilitate the higher classi cation performance with the segmentation results. In conclusion, the proposed active contour segmentation model with adaptive parameters has the potential to deal with various di erent patterns of tissue and tumor e ectively.
Lesion segmentation is one of the key technologies for computer-aided diagnosis (CAD) system. In this paper, we propose a robust region-based active contour model (ACM) with point classification to segment high-variant breast lesion in ultrasound images. First, a local signed pressure force (LSPF) function is proposed to classify the contour points into two classes: local low contrast class and local high contrast class. Secondly, we build a sub-model for each class. For low contrast class, the sub-model is built by combining global energy with local energy model to find a global optimal solution. For high contrast class, the sub-model is just the local energy model for its good level set initialization. Our final energy model is built by adding the two sub-models. Finally, the model is minimized and evolves the level set contour to get the segmentation result. We compare our method with other state-of-art methods on a very large ultrasound database and the result shows that our method can achieve better performance.