Contour-Based Image Segmentation Using Selective Visual Attention

Engin Mendi, Mariofanna Milanova

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DOI: 10.4236/jsea.2010.38092                

Abstract

In many medical image segmentation applications identifying and extracting the region of interest (ROI) accurately is an important step. The usual approach to extract ROI is to apply image segmentation methods. In this paper, we focus on extracting ROI by segmentation based on visual attended locations. Chan-Vese active contour model is used for image segmentation and attended locations are determined by SaliencyToolbox. The implementation of the toolbox is extension of the saliency map-based model of bottom-up attention, by a process of inferring the extent of a proto-object at the attended location from the maps that are used to compute the saliency map. When the set of regions of interest is selected, these regions need to be represented with the highest quality while the remaining parts of the processed image could be represented with a lower quality. The method has been successfully tested on medical images and ROIs are extracted.

Keywords

Active Contours, Selective Visual Attention, Image Segmentation, Telemedicine

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	D. Mumford and J. Shah, “Optimal Approximation by Piecewise Smooth Functions and Associated Variationol Problems,” Communications on Pure and Applied Mathe- matics, Vol. 42, 1989, pp. 577-685.
[2]	D. Mumford and J. Shah, “Boundary Detection by Minimizing Functionals, I,” Proceedings of IEEE Con- ference on Computer Vision and Pattern Recognition, San Francisco, 1985, pp. 22-26.
[3]	J. Shah, “Piecewise Smooth Approximations of Functions,” Calculus of Variations and Partial Differential Equations, Vol. 2, 1994, pp. 315-328.
[4]	T. F. Chan and L. A. Vese, “Active Contours without Edges,” IEEE Transactions on Image Processing, Vol. 10, No. 2, 2001, pp. 266-277.
[5]	D. Walters, U. Rutishauser, C. Koch and P. Perona, “Selective Visual Attention Enables Learning and Recognition of Multiple Objects in Cluttered Scenes,” Computer Vision and Image Understanding, Vol. 100, No. 1-2, 2005, pp. 41-63.
[6]	M. Milanova, S. Rubin, R. Kountchev, V. Todorov and R. Kountcheva, “Combined Visual Attention Model for Video Sequences,” IEEE ICPR’2008 International Con- ference on Pattern Recognition, Tampa, 2008, pp. 1-4.
[7]	D. Walter, “Interactions of Visual Attention and Object Recognition: Computational Modeling, Algorithms, and Psychophysics,” PhD Thesis, California Institute of Technology, Pasadena, 2006.
[8]	X.-P. Hu, “Hot Spot Detection based on Feature Space Representation of Visual Search,” IEEE Transactions on Medical Images, Vol. 22, No. 9, 2003, pp. 1152-1162.
[9]	A. Kountchev and A. Mironov, “Algorithms for Pyramid Image Decomposition,” In: N. Mastorakis Ed., Intelligent Systems and Computer Science, WSEAS Press, Danvers, 1999, pp. 196-200.
[10]	M. Mancas, “Image Perception: Relative Influence of Bottom-Up and Top-Down Attention,” Attention in Cog- nitive Systems, Lecture Notes in Computer Science, Vol. 5395, Greece, 2008, pp. 212-226.
[11]	D. Walther and C. Koch, “Modeling Attention to Salient Proto-Objects,” Neural Networks, Vol. 19, No. 9, 2006, pp. 1395-1407.
[12]	C. Koch and S. Ullman, “Shifts in Selective Visual- Attention—Towards the Underlying Neural Circuitry,” Human Neurobiology, Vol. 4, No. 4, 1985, pp.219-227.
[13]	E. Mendi and M. Milanova, “Image Segmentation with Active Contours Based on Selective Visual Attention,” 8th WSEAS International Conference on Signal Pro- cessing (SIP’09), Istanbul, 2009, pp. 79-84.
[14]	K. Fundana, N. C. Overgaard and A. Heyden, “Deformable Shape Priors in Chan-Vese Segmentation of Image Sequences,” Proceedings International Conference on Image Processing, San Antonio, Vol. 1, 2007, pp. 285- 288.
[15]	L. Itti, C. Koch and E. Niebur, “A Model of Saliency-Based Visual Attention for Rapid Scene Analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 11, 1998, pp. 1254- 1259.
[16]	C. Koch and L. Itti, “Computational Modeling of Visual Attention,” Nature Reviews Neuroscience, Vol. 2, 2001, pp. 194-203.
[17]	D. Walther, U. Rutishauser, C. Koch and P. Perona, “On the Usefulness of Attention for Object Recognition,” Workshop on Attention and Performance in Compu- tational Vision at ECCV, Prague, 2004, pp. 96- 103.
[18]	M. Mancas, B. Gosselin and B Macq, “Perceptual Image Representation,” EURASIP Journal of Image and Video Processing, Vol. 2007, 2007, pp. 1-9.
[19]	R. Kountchev, S. Rubin, M. Milanova, V. Todorov and R. Kountcheva, “Non-Linear Image Representation Based on IDP with NN,” WSEAS Transactions on Signal Processing, Istanbul, Vol. 5, No. 9, 2009, pp. 315-325.