Towards Automatic Image Segmentation Using Optimised Region Growing Technique

  • Authors:

  • Mamoun Alazab;Mofakharul Islam;Sitalakshmi Venkatraman

  • Affiliations:

  • Internet Commerce Security Lab, University of Ballarat, Australia;Internet Commerce Security Lab, University of Ballarat, Australia;Graduate School of ITMS, University of Ballarat, Australia

  • Venue:
  • AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
  • Year:
  • 2009

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Abstract

Image analysis is being adopted extensively in many applications such as digital forensics, medical treatment, industrial inspection, etc. primarily for diagnostic purposes. Hence, there is a growing interest among researches in developing new segmentation techniques to aid the diagnosis process. Manual segmentation of images is labour intensive, extremely time consuming and prone to human errors and hence an automated real-time technique is warranted in such applications. There is no universally applicable automated segmentation technique that will work for all images as the image segmentation is quite complex and unique depending upon the domain application. Hence, to fill the gap, this paper presents an efficient segmentation algorithm that can segment a digital image of interest into a more meaningful arrangement of regions and objects. Our algorithm combines region growing approach with optimised elimination of false boundaries to arrive at more meaningful segments automatically. We demonstrate this using X-ray teeth images that were taken for real-life dental diagnosis.