Biology Inspired Image Segmentation using Methods of Artificial Intelligence

Abstract

In recent years,many efforts have been devoted to image segmentation. Although for a man general image segmentation is considered an easy task, for computers it is still considered to be difficult, computationally intensive and still unresolved task. This work presents an innovative algorithm combining theory of artificial intelligence and knowledge of human eye anatomy. The resulting algorithm has not ambitions to be universal like human brain but can be trained and perform on selected domain. The effectiveness of the algorithm is demonstrated on the selected examples.

Share and Cite:

R. Burget, V. Uher and J. Masek, "Biology Inspired Image Segmentation using Methods of Artificial Intelligence," Journal of Software Engineering and Applications, Vol. 5 No. 12B, 2012, pp. 172-174. doi: 10.4236/jsea.2012.512B033.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] J. Tang, A color image segmentation algorithm based on region growing, 2nd International Conference on Com-puter Engineering and Technology (ICCET), 2010
[2] ChuanLong Li; Ying Li; XueRui Wu, Novel Fuzzy C-Means Segmentation Algorithm for Image with the Spatial Neighborhoods, 2nd International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE), 2012
[3] Hasanzadeh, M.; Kasaei, S.; Mohseni, H., A New Fuzzy Connectedness Relation for Image Segmentation, 3rd International Conference on Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008.
[4] M Rousson, C Xu, A general framework for image seg-mentation using ordered spatial dependency, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006, Lecture Notes in Computer Science, 2006, Volume 4191/2006
[5] Yong Xia, A General Image Segmentation Model and its Application, Conference on Image and Graphics, 2009. ICIG '09. Fifth International
[6] R. Nock, F. Nielsen (2004), "Statistical Region Merging", IEEE Trans. Pattern Anal. Mach. Intell. 26 (11): 1452-1458
[7] J. R. Quinlan, Induction of Decision Trees, Journal Machine Learning, 1986
[8] Cortes, Corinna; and Vapnik, Vladimir N.; "Support-Vector Networks", Machine Learning, 20, 1995.
[9] L. Breiman: Random Forests. Machine Learning 45(1): 5-32 (2001)
[10] Cover TM, Hart PE, "Nearest neighbor pattern classification". IEEE Transac-tions on Information Theory 13 (1): 21–27.
[11] RadimBurget, PetrCika, Martin Zukal, Jan Masek: Automated localization of Temporomandibular Joint Disc in MRI images. Telecommunications and Signal Processing (TSP), 2011 34th International Con-ference on: p.: 413-41
[12] Burget, R., Uher, V., Masek, J., Trainable Segmentation Based on Local-level and Segment-level Feature Extraction, 2012 IEEE Interna-tional Symposium on Biomedical Imaging: From nano to Macro, 2012
[13] Uher, V., Burget, R. , Automatic 3D Segmentation of Human Brain Images Using Data-mining Techniques, 35th International Conference on Telecommunications and Signal Processing (TSP 2012), 2012
[14] Burget, R., Karasek, J., Smekal, Z., et al., RapidMiner Image Processing Extension: A Platform for Collaborative Research, 33rd International Confer-ence on Telecommunication and Signal Processing Lo-cation: Vienna, AUSTRIA Date: AUG 17-20, 2010
[15] Burget, R.; Cika, P.; Zukal, M.,Automated Localization of Temporomandibular Joint Disc in MRI Images 34th International Conference on Telecommunications and Signal Processing (TSP), Budapest, HUNGARY, AUG 18-20, 2011

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.