A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- va...A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- variogram texture features are extracted from the image and a seeded region growing algorithm is run on these feature spaces. With a given Region of Interest (ROI), a seed point is automatically se-lected based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing ‘explosion’. This algorithm is tested on 12 series of 3D ab-dominal MR images.展开更多
A texture image segmentation based on nonlinear diffusion is presented. The scale of texture can be measured during the process of nonlinear diffusion. A smooth 5-channel vector image with edge preserved, which is com...A texture image segmentation based on nonlinear diffusion is presented. The scale of texture can be measured during the process of nonlinear diffusion. A smooth 5-channel vector image with edge preserved, which is composed of intensity, scale and orientation of texture image, can be achieved by coupled nonlinear diffusion. A multi-channel statistical region active contour is employed to segment this vector image. The method can be seen as a kind of unsupervised segmentation because parameters are not sensitive to different texture images. Experimental results show its high efficiency in the semiautomatic extraction of texture image.展开更多
影像分割是面向对象影像分析中的重要步骤。为了提高高分辨率遥感影像(high-resolution remote sensing image,HRI)分割算法的性能,提出一种新的影像分割算法,包含种子确定、基于种子区域生长(seeded region growing,SRG)的过分割(advan...影像分割是面向对象影像分析中的重要步骤。为了提高高分辨率遥感影像(high-resolution remote sensing image,HRI)分割算法的性能,提出一种新的影像分割算法,包含种子确定、基于种子区域生长(seeded region growing,SRG)的过分割(advanced SRG,ASRG)和层次区域生长(hierarchical region growing,HRG)3个步骤。利用Gabor纹理特征定义纹理均匀性,将种子自动放置在HRI中同一纹理组成区域的中心位置;在SRG阶段,将HRI光谱信息与斑块形状信息相结合,提出了一种新的合并规则,以提高SRG过分割的精度与分割结果中各个斑块排列的紧凑性;在HRG阶段,提出了一种自适应的阈值,可以更好地保持多尺度分割的特性;在实验部分,采用3景HRI验证了上述方法。利用监督的影像分割评价方法定量评价了该方法的分割精度,并与另外2种主流的遥感影像分割算法进行了对比。结果表明,该方法可以得到令人满意的分割效果。展开更多
文摘A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- variogram texture features are extracted from the image and a seeded region growing algorithm is run on these feature spaces. With a given Region of Interest (ROI), a seed point is automatically se-lected based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing ‘explosion’. This algorithm is tested on 12 series of 3D ab-dominal MR images.
文摘A texture image segmentation based on nonlinear diffusion is presented. The scale of texture can be measured during the process of nonlinear diffusion. A smooth 5-channel vector image with edge preserved, which is composed of intensity, scale and orientation of texture image, can be achieved by coupled nonlinear diffusion. A multi-channel statistical region active contour is employed to segment this vector image. The method can be seen as a kind of unsupervised segmentation because parameters are not sensitive to different texture images. Experimental results show its high efficiency in the semiautomatic extraction of texture image.
文摘影像分割是面向对象影像分析中的重要步骤。为了提高高分辨率遥感影像(high-resolution remote sensing image,HRI)分割算法的性能,提出一种新的影像分割算法,包含种子确定、基于种子区域生长(seeded region growing,SRG)的过分割(advanced SRG,ASRG)和层次区域生长(hierarchical region growing,HRG)3个步骤。利用Gabor纹理特征定义纹理均匀性,将种子自动放置在HRI中同一纹理组成区域的中心位置;在SRG阶段,将HRI光谱信息与斑块形状信息相结合,提出了一种新的合并规则,以提高SRG过分割的精度与分割结果中各个斑块排列的紧凑性;在HRG阶段,提出了一种自适应的阈值,可以更好地保持多尺度分割的特性;在实验部分,采用3景HRI验证了上述方法。利用监督的影像分割评价方法定量评价了该方法的分割精度,并与另外2种主流的遥感影像分割算法进行了对比。结果表明,该方法可以得到令人满意的分割效果。
