부산대학교 도서관

Extracting the structures of interest accurately is one of the main challenges in medical imaging segmentation. Statistical models of shape are a promising approach for robust and automatic segmentation of medical image data. This work describes the construction of a statistical shape model of the Radius bone. For 3-D model-based approaches, however, building the 3-D shape model from a training data set of segmented instances of an object is a major challenge and currently remains an open problem. In this study, we propose an active contour image segmentation method for three-dimensional (3-D) medical images. Our dataset contains T1-weighted images of hand wrist in coronal view. Such images are usually acquired in 9 slices, but we also used 27 slices images in which the spatial resolution is improved by reducing the in depth from 3mm to 1mm. In this study we use 27-slices MRI images to segment radius bone due to their higher resolutions in comparison to 9-slices images. First, using 2D active contour algorithm, radius bone is segmented in coronal slices automatically. Then, a statistical model of radius bone is derived and its mean model is used as the initial mask for 3D active contour algorithm, and 9-slices images are segmented using this algorithm. To compare the 2D and 3D active contour algorithms, 27-slices images are segmented through produced statistical atlas of mean model. Comparison of obtained segmentation and manual segmentation shows that segmentation accuracy in 9-slices images which use mean model will be increased from 75.68% to 91.57%. Acquisition of 9-slicese images takes a shorter time (1/3) in comparison to 27-slices images; therefore, we also derived the statistical model of 9-slices images. In the future works we utilize the proposed approach as part of a computer-aided diagnosis system for bone age estimation.