Ultrasound imaging is a popular and non-invasive tool used in the diagnoses of liver disease. Cirrhosis is a chronic liver disease and it can advance to liver cancer. Early detection and appropriate treatment are crucial to prevent liver cancer. However, ultrasound image analysis is very challenging, because of the low signal-to-noise ratio of ultrasound images. To achieve the higher classification performance, selection of training regions of interest (ROIs) is very important that effect to classification accuracy. The purpose of our study is cirrhosis detection with high accuracy using liver ultrasound images. In our previous works, training ROI selection by MILBoost and multiple-ROI classification based on the product rule had been proposed, to achieve high classification performance. In this article, we propose self-training method to select training ROIs effectively. Evaluation experiments were performed to evaluate effect of self-training, using manually selected ROIs and also automatically selected ROIs. Experimental results show that self-training for manually selected ROIs achieved higher classification performance than other approaches, including our conventional methods. The manually ROI definition and sample selection are important to improve classification accuracy in cirrhosis detection using ultrasound images.
Application of machine vision is expected for efficiency and objectivity of inspection in various fields. Automation of visual inspection for asphalt pavement surface images is also expected, but it is difficult because of unexpected objects, non-uniform illumination and irregularities in the pavement surface. Many of conventional approaches are based on state-of-the-arts. However, there is a problem that the application conditions of these is limited. In this article, we proposed a new method based on state-of-the-art and machine learning for crack detection from asphalt pavement surface images. The classifier of the proposed method is the linear support vector machine, and it uses features proposed in the conventional study that is one of the state-of-the-art approaches. The proposed system need not a large number of training data, unlike deep learning architectures. It is easy to train the classifier to detect cracks using a GUI tool developed by authors. Quantitative evaluation using 100 road surface images obtained by mobile mapping system was performed to compare with our conventional method as one of state-of-the-art approaches. Experiments show that our proposed method clearly outperforms the state-of-the-art approach.
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