BackgroundClassification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers to discriminate benign and malignant tumors in BUS images.MethodThe proposed descriptor, namely the phased congruency-based binary pattern (PCBP) is an oriented local texture descriptor that combines the phase congruency (PC) approach with the local binary pattern (LBP). The support vector machine (SVM) is further applied for the tumor classification. To verify the efficiency of the proposed PCBP texture descriptor, we compare the PCBP with other three state-of-art texture descriptors, and experiments are carried out on a BUS image database including 138 cases. The receiver operating characteristic (ROC) analysis is firstly performed and seven criteria are utilized to evaluate the classification performance using different texture descriptors. Then, in order to verify the robustness of the PCBP against illumination variations, we train the SVM classifier on texture features obtained from the original BUS images, and use this classifier to deal with the texture features extracted from BUS images with different illumination conditions (i.e., contrast-improved, gamma-corrected and histogram-equalized). The area under ROC curve (AUC) index is used as the figure of merit to evaluate the classification performances.Results and conclusionsThe proposed PCBP texture descriptor achieves the highest values (i.e. 0.894) and the least variations in respect of the AUC index, regardless of the gray-scale variations. It’s revealed in the experimental results that classifications of BUS images with the proposed PCBP texture descriptor are efficient and robust, which may be potentially useful for breast ultrasound CADs.

Due to the speckle noises, low contrast and blurry boundaries in breast ultrasound (BUS) images, extraction of the boundaries in BUS images is always a challenging task. To solve this problem, a novel phase-based active contour (PBAC) model is proposed. First, we utilize the local phase information and apply the phase asymmetry approach to form a new edge indicator, which dramatically increases the robustness to intensity inhomogeneous. Then, a novel phase-based edge indicator is incorporated into a various level set formulation with the local region-based segmentation energy. Experiments are performed on both synthetic and real BUS images. The results show that the proposed PBAC model outperforms the state-of-art methods both qualitatively and quantitatively.

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Based on the new structure of the P1 training symbol, channel estimation is proposed for the second generation digital terrestrial distribution system standardized by the Digital Video Broadcasting Consortium (DVB-T2). The solution for the channel estimation was presented. In addition, the simulation results show MSE of the proposed channel estimation.