In this paper, we propose novel feature extraction techniques which can provide a high accuracy rate of mass classification in the computer-aided lesion diagnosis of breast tumor. Totally 290 features were extracted using the newly developed border irregularity feature extractor as well as multiple sonographic features based on the breast imaging-reporting and data system (BI-RADS) lexicons. To demonstrate the performance of the proposed features, 4,107 ultrasound images containing 2,508 malignant cases were used. The clinical results demonstrate that the proposed feature combination can be an integral part of ultrasound CAD systems to help accurately distinguish benign from malignant tumors.
Early detection of breast tumor is critical in determining the best possible treatment approach. Due to its superiority compared with mammography in its possibility to detect lesions in dense breast tissue, ultrasound imaging has become an important modality in breast tumor detection and classification. This paper discusses the novel Fourier-based shape feature extraction techniques that provide enhanced classification accuracy for breast tumor in the computer-aided B-mode ultrasound diagnosis system. To demonstrate the effectiveness of the proposed method, experiments were performed using 4,107 ultrasound images with 2,508 malignancy cases. Experimental results show that the breast tumor classification accuracy of the proposed technique was 15.8%, 5.43%, 17.32%, and 13.86% higher than the previous shape features such as number of protuberances, number of depressions, lobulation index, and dissimilarity, respectively.
Existing binary join based plans may be suboptimal for important, emerging applications. Typical query optimizers enumerate plans using binary joins only. In this paper, we introduce the multi-way join aware optimizer in SAP HANA. The naive way to extend the existing query optimizer to be aware of multi-way joins (
m
-way joins for short) is to enumerate
m
-way joins on top of a traditional binary join enumeration framework. However, many different binary joins correspond to the same
m
-way join. Thus, unnecessary join enumerations would be required for such naive integration. To solve this problem, we introduce the new concept of an
m
-way join unit and explain how the construction of join units is plugged into the SAP HANA query optimizer. We also provide a series of optimizer enhancements by exploiting
m
-way join unit characteristics. Using TPC-H and our customer workloads, we showcase the superiority of our
m
-way join aware optimizer.
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