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.
Cross-sectional data suggest that the degree of coronary atherosclerosis is associated with aerobic fitness. However, there are limited longitudinal data addressing whether aerobic fitness is a predictor of coronary atherosclerosis progression. This study investigated whether peak oxygen consumption is related to a longitudinal increase in coronary calcium scores. Study subjects were voluntary participants in a health screening program who underwent a cardiopulmonary function test and repeated coronary calcium scoring. Individuals with clinical cardiovascular disease were excluded. The final sample included 4843 subjects with 14,856 records. The treadmill exercise test was performed using a modified Bruce protocol and Agatston coronary artery calcium (CAC) scores were measured using multi-detector CT. The mean age of the participants was 52 ± 6 years and 4.7 % were female. In a multi-level mixed effect regression model, increased CAC scores over time were significantly less likely in individuals with a higher VO2peak after adjusting for age, gender, hypertension, HbA1c, smoking status and LDL cholesterol levels (p < 0.001). Aerobic fitness has a protective effect on the progression of coronary atherosclerosis in an asymptomatic middle-aged population.
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.
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