Documentation/Nightly/Modules/GrowCutSegmentation

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Introduction and Acknowledgements

Authors: Harini Veeraraghavan, Jim Miller Contacts:

• veerarag@ge.com

Module Description

Grow Cut Segmentation is a competitive region growing algorithm using cellular automata. The algorithm works by using a set of user input scribbles for foreground and background. For N-class segmentation, the algorithm requires a set of scribbles corresponding the N classes and a scribble for a don't care class. The algorithm executes as follows: Using the "user input scribbles", the algorithm automatically computes a region of interest that encompass the scribbles. It is important that the scribbles do not include a single voxel out of your region of interest as the Next, the algorithm iteratively tries to label all the pixels in the image using the label of pixels in the user scribbled portions of the image. The algorithm converges when all the pixels in the ROI are labeled, and no pixel can change it's label any more. Individual pixels are labeled by computing a weighted similarity metric of a pixel with all its neighbors, where the weights correspond to the neighboring pixel's strength. The neighbor that results in the largest weight greater than the given pixel's strength, confers its label to the given pixel. After the segmentation, the user can edit the segmentation by providing additional gestures in the image as illustrated in the figure below.