Automated Growcut for segmentation of endoscopic images

Capsule endoscopy (CE), introduced as a modality for non-invasive examination of entire gastrointestinal tract, demands for an efficient computer-aided decision making system to relieve the physician from the responsibility of screening around 60,000 video frames per patient. An automatic and robust segmentation algorithm can aid the automation of CE screening and decision making procedure. In this paper, we propose a new segmentation algorithm based on GrowCut and apply the algorithm for CE images containing bleeding. To substitute the manual seed input in traditional GrowCut segmentation, the proposed Automated GrowCut (AGC) algorithm initially segments the images using clustering. The cluster centroids, subsequently labeled as bleeding, non-bleeding and background by a trained SVM classifier, serve as seeds for the GrowCut segmentation. A comprehensive evaluation and comparison with respect to ground truth exhibits that the proposed method can achieve a Dice Similarity Coefficient of 0.81, comparable to the interactive GrowCut, requiring only 13.96% of the computation time of interactive GrowCut. The comparison with two other state-of-the-art unsupervised segmentation methods, unsupervised GrowCut and Fuzzy c-means, further justifies the suitability of the proposed method for automated segmentation and annotation of CE images.