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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.9, No.9, Sep. 2017

Appearance-based Salient Features Extraction in Medical Images Using Sparse Contourlet-based Representation

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Author(s)

Rami Zewail, Ahmed Hag-ElSafi

Index Terms

Salient features;multiscale;appearance;sparse;contourlet

Abstract

Medical experts often examine hundreds of x-ray images searching for salient features that are used to detect pathological abnormalities. Inspired by our understanding of the human visual system, automated salient features detection methods have drawn much attention in the medical imaging research community. However, despite the efforts, detecting robust and stable salient features in medical images continues to constitute a challenging task. This is mainly attributed to the complexity of the anatomical structures of interest which usually undergo a wide range of rigid and non-rigid variations.
In this paper, we present a novel appearance-based salient feature extraction and matching method based on sparse Contourlet-based representation. The multi-scale and directional capabilities of the Contourlets is utilized to extract salient points that are robust to noise, rigid and non-rigid deformations. Moreover, we also include prior knowledge about local appearance of the salient points of the structure of interest. This allows for extraction of robust stable salient points that are most relevant to the anatomical structure of interest. 

Cite This Paper

Rami Zewail, Ahmed Hag-ElSafi,"Appearance-based Salient Features Extraction in Medical Images Using Sparse Contourlet-based Representation", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.9, pp.1-10, 2017.DOI: 10.5815/ijigsp.2017.09.01

Reference

[1]C. Schmid, R. Mohr and C. Bauckhage, “Evaluation of Interest Point Detectors,” International Journal of Computer Vision, vol. 37(2), pp.151-172, 2000.

[2]C. Harris, M. Stephens, “A combined corner and edge detector,” Proceedings of Alvey Vision Conference, 1988.

[3]D. Lowe, “Object Recognition from Local Scale-Invariant Features,” Proceedings of 7th IEEE International Conference on Computer Vision, vol. 2, pp. 1150-1157, 1999.

[4]Q. Tian, N. Sebe, M. S. Lew, E. Loupias, and T. S. Huang, “Image retrieval using wavelet-based salient points,” Journal of Electronic Imaging, vol. 10(4), pp. 835-849, 2001.

[5]E. Loupias, N. Sebe, “Wavelet-based Salient Points: Applications to Image Retrieval Using Color and Texture Features,” Proceedings of 4th Int. Conf. on Visual Information Systems, pp. 223-232, 2000.

[6]L. Itti, C. Koch and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 20(11), pp. 1254-1259, 1998.

[7]C. Wu, Q. Wang, “A Novel Approach for Interest Point Detection Based on Phase Congruency,” Proceedings of TENCON 2005, pp. 1-6, 2005.

[8]X. Gao, F. Sattar, R. Venkateswarlu, “Multiscale Corner Detection of Gray Level Images Based on Log-Gabor Wavelet Transform,” IEEE Transactions on Circuits Systems and  Video Technology, vol. 17(7), pp. 868-875, 2007.

[9]P. Kovesi, “Phase Congruency Detects Corners and Edges,” Australian Pattern Recognition Society Conference: DICTA 2003, pp. 309-318, 2003.

[10]A. Webb, Statistical Pattern Recognition. 2nd edition, 2002.

[11]Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 733–740. IEEE (2012).

[12]Hangbing Gao, Yunyang Yan, Youdong Zhang, Jingbo Zhou, Suqun Cao, and Jianxun Xue “Automatic Segmentation of Nature Object Using Salient Edge Points Based Active Contour,” Mathematical Problems in Engineering, Volume 2015 (2015), Article ID 174709.

[13]Stella Vetova, Ivan Ivanov, Content-based Image Retrieval Using the Dual-Tree Complex Wavelet Transform,” Proceedings of MCSI '14 Proceedings of the 2014 International Conference on Mathematics and Computers in Sciences and in Industry, pp. 165-170. 

[14]K. M. Yi, E. Trulls, V. Lepetit and P. Fua, “LIFT: Learned Invariant Feature Transform,” European Conference on Computer Vision (ECCV) 2016.

[15]Mark Brown, David Windridge, Jean-Yves Guillemaut, “A generalized framework for saliency-based point feature detection,” Computer Vision and Image Understanding,  Volume 157, April 2017, pp. 117–137.

[16]M. Nixon, A, Aguado. Feature Extraction and Image Processing. 2nd edition, Elsevier, 2008.

[17]Mahesh, Subramanyam M. V,"Feature Based Image Mosaic Using Steerable Filters and Harris Corner Detector", IJIGSP, vol.5, no.6, pp.9-15, 2013.DOI: 10.5815/ijigsp.2013.06.02.

[18]Omprakash S. Rajankar, Uttam D. Kolekar, “Scale Space Reduction with Interpolation to Speed up Visual Saliency Detection", IJIGSP, vol.7, no.8, pp.58-65, 2015.DOI: 10.5815/ijigsp.2015.08.07