Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.
The titanium carbides are potential candidates to achieve both high hardness and refractory property. We carried out a structural search for titanium carbides at three pressures of 0 GPa, 30 GPa and 50 GPa. A phase diagram of the Ti-C system at 0 K was obtained by elucidating formation enthalpies as a function of compositions, and their mechanical and metallic properties of titanium carbides were investigated systematically. We also discussed the relation of titanium concentration to the both mechanical and metallic properties of titanium carbides. It has been found that the average valence electron density and tractility improved at higher concentrations of titanium, while the degree of covalent bonding directionality decreased. To this effect, the hardness of titanium carbide decreases as the content of titanium increases. Our results indicated that the titanium content significantly affected the metallic properties of the Ti-C system. 相似文献