To model both the object and the scene in an image, Russell et al. [32] proposed to use regions resulting from multiple unsupervised image segmentations to represent an image as an aggregate of sub-images.
This section looks at the segmentation of multiple image sequences, in which all process steps are considered: image segmentation, blob computation, optical flow computation followed by segmentation, cluster recognition, and finally, cluster-blob association.
We use compressed sensing image reconstruction, and process large image arrays using deformable registration, perform segmentation based on multiple image contrasts including diffusion tensor imaging, as well as voxel based analysis, and graph analysis for connectomics.
In [29], Tao et al. integrated the multiple piecewise constant with the GAC model, which can also overcome the problem of intensity inhomogeneity and multiple objects for image segmentation.
First, our atlas-based segmentation uses multiple image contrasts to drive the spatial normalization.
Unlike traditional single-image segmentation, the co-segmentation method can segment multiple images jointly rather than independently segmenting each image based on the co-occurrence of objects in the images [16].
To solve multiple images in co-segmentation, the heat was diffused among the common objects by foreground similarity.
The multiple image modalities were registered to the T1 with boundary-based registration (BBR) and the segmentation of WMH from healthy WM was accomplished with a multispectral Gaussian classifier for each subject based on the atlas values.
What Segmentation.Network plays back is a random collage of image segmentations at their original position.
We discuss the basic principles of clinical image segmentation, including coregistration of multiple imaging datasets to create an anatomic model of interest.
