SLIC in a defined mask with applications to medical imaging

Supervoxel methods are effective for reducing an image or volume into a set of locally similar regions which has a number of advantages to pixel based methods for segmentation and graph based methods. Simple linear iterative clustering (SLIC) is an effective supervoxel method but is limited to rectangular volumes. In this paper we reformulate the SLIC algorithm to work more effectively in predefined regions-of-interest. The key contribution is the reformulation of the seed point initialisation. This method is applied to an example image with source code and a live demo available. There are a number of applications to computing SLIC inside an mask region including assessment of pathological subregions.