Learn More
RATIONALE AND OBJECTIVES An atlas-based automated liver segmentation method from three-dimensional computed tomographic (3D CT) images has been developed. The method uses two types of atlases, a probabilistic atlas (PA) and a statistical shape model (SSM). MATERIALS AND METHODS Voxel-based segmentation with a PA is first performed to obtain a liver(More)
Automatic tumor detection and segmentation is essential for the computer-aided diagnosis of live tumors in CT images. However, it is a challenging task in low-contrast images as the low-level images are too weak to detect. In this paper, we propose a new method for the automatic detection of liver tumors. We first adaptively enhance the intensity contrast(More)
This paper proposes an adaptive color independent components based SIFT descriptor (termed CIC-SIFT) for image classification. Our motivation is to seek an adaptive and efficient color space for color SIFT feature extraction. Our work has two key contributions. First, based on independent component analysis (ICA), an adaptive and efficient color space is(More)
In this paper, we incorporate the concept of Multiple Kernel Learning (MKL) algorithm, which is used in object categorization, into human tracking field. For efficiency, we devise an algorithm called Multiple Kernel Boosting (MKB), instead of directly adopting MKL. MKB aims to find an optimal combination of many single kernel SVMs focus-ing on different(More)