Automated neurosurgical video segmentation and retrieval system
Engin Mendi, Songul Cecen, Emre Ermisoglu, Coskun Bayrak
.
DOI: 10.4236/jbise.2010.36084   PDF    HTML     4,324 Downloads   8,220 Views   Citations

Abstract

Medical video repositories play important roles for many health-related issues such as medical imaging, medical research and education, medical diagnostics and training of medical professionals. Due to the increasing availability of the digital video data, indexing, annotating and the retrieval of the information are crucial. Since performing these processes are both computationally expensive and time consuming, automated systems are needed. In this paper, we present a medical video segmentation and retrieval research initiative. We describe the key components of the system including video segmentation engine, image retrieval engine and image quality assessment module. The aim of this research is to provide an online tool for indexing, browsing and retrieving the neurosurgical videotapes. This tool will allow people to retrieve the specific information in a long video tape they are interested in instead of looking through the entire content.

Share and Cite:

Mendi, E. , Cecen, S. , Ermisoglu, E. and Bayrak, C. (2010) Automated neurosurgical video segmentation and retrieval system. Journal of Biomedical Science and Engineering, 3, 618-624. doi: 10.4236/jbise.2010.36084.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Cecen, S. (2009) Histogram based video segmentation and key frame extraction on SOM and DFT. Master’s Thesis, University of Arkansas, Little Rock.
[2] Mendi, E. and Bayrak, C. (2010) Shot boundary detection and key frame extraction from video sequences. Elsevier Information Sciences, 2010.
[3] Pavan, M. and Pelillo, M. (2003) Dominant sets and hiera-rchical clustering. Proceedings of the 9th European Conference on Computer Vision, 362-369.
[4] Pavan, M. and Pelillo, M. (2005) Efficient out-of-sample extension of dominant-set clusters. Advances in Neural Information Processing Systems, 17, 1057-1064.
[5] Mendi, E. and Bayrak, C. (2010) Shot boundary de- tection and key frame extraction using salient region detection and structural similarity. The 48th ACM Sou- theast Conference, Oxford, Mississippi, 15-17 April, 2010.
[6] Lehmann, T. M., Mller, H., Tian, Q., Galatsanos, N.P. and Mlynek, D. (2005) Augmented medical image management for integrated healthcare solutions.
[7] Mendi, E. and Bayrak, C. (2010) Performance analysis of color image retrieval. The 3rd International Congress on Image and Signal Processing (CISP'10), Yantai, 2010.
[8] Wang, Z. Bovik, A.C. Sheikh, H.R. and Simoncelli, E.P. (2004) Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Pro- cessing, 13(4), 600-612.
[9] Li J. and Wang, J.Z. (2003) Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9), 1075-1088.
[10] Wang J.Z., Li, J. and Wiederhold G. (2001) SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. The IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 947-963.
[11] Chono, K., Lin, Y.-C., Varodayan, D., Miyamoto, Y. and Girod, B. (2008) Reduced-reference image quality ass- essment using distributed source coding. IEEE Inter- national Conference on Multimedia and Expo, 2008.
[12] Zhang, L., Chen, L., Jing, F. and Ma, W.-Y. (2006) Enjoy photo a vertical image search engine for enjoying high-quality photos. The 14th ACM International Confer- ence on Multimedia, ACM Press, Santa Barbara.
[13] Achanta, R., Hemami, S., Estrada, F. and Süsstrunk, S. (2009) Frequency-tuned salient region detection. IEEE International Conference on Computer Vision and Patt- ern Recognition (CVPR), Miami.
[14] Sheikh, H.R. and Bovik, A.C. (2006) Image information and visual quality. IEEE Transactions on Image Pro- cessing, 15(2), 430-444.
[15] Sheikh, H.R., Wang, Z., Cormack, L. and Bovik, A.C. (2005) Live Image Quality Assessment Database Release 2. http:// live.ece.utexas.edu/research/quality.
[16] Mendi, E. and Milanova, M. (2010) Image quality assessment based on salient region detection. Journal of Visual Communication and Image Representation, Elsevier Ltd., 2010.

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.