Relationship between aerobic fitness and progression of coronary atherosclerosis.

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.

Multiobjective evolutionary optimization for tumor segmentation of breast ultrasound images.

This paper proposes a robust multiobjective evolutionary algorithm (MOEA) to optimize parameters of tumor segmentation for ultrasound breast images. The proposed algorithm employs efficient schemes for reinforcing proximity to Pareto-optimal and diversity of solutions. They are designed to solve multiobjective problems for segmentation accuracy and speed.

Prediction of 4-year risk for coronary artery calcification using ensemble-based classification.

The progression of coronary artery calcification (CAC) has been regarded as an important risk factor of coronary artery disease (CAD), which is the biggest cause of death. Because CAC occurrence increases the risk of CAD by a factor of ten, the one whose coronary artery is calcified should pay more attention to the health management. However, performing the computerized tomography (CT) scan to check if coronary artery is calcified as a regular examination might be inefficient due to its high cost.

Fourier-based shape feature extraction technique for computer-aided B-Mode ultrasound diagnosis of breast tumor.

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.

Identifying relatively high-risk group of coronary artery calcification based on progression rate: statistical and machine learning methods.

Coronary artery calcification (CAC) score is an important predictor of coronary artery disease (CAD), which is the primary cause of death in advanced countries. Early prediction of high-risk of CAC based on progression rate enables people to prevent CAD from developing into severe symptoms and diseases. In this study, we developed various classifiers to identify patients in high risk of CAC using statistical and machine learning methods, and compared them with performance accuracy.