“GrowCut” - Interactive Multi-Label N-D Image Segmentation By Cellular

Automata



Abstract

In this project we describe a novel algorithm for interactive multilabel

segmentation of N-dimensional images. Given a small number

of user-labelled pixels, the rest of the image is segmented automatically

by a Cellular Automaton. The process is iterative, as

the automaton labels the image, user can observe the segmentation

evolution and guide the algorithm with human input where the segmentation

is difficult to compute. In the areas, where the segmentation

is reliably computed automatically no additional user effort

is required. Results of segmenting generic photos and medical images

are presented. Our experiments show that modest user effort

is required for segmentation of moderately hard images.