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Retrieving Similar Segmented Objects Using Motion Descriptors

Authors: Konstantinos C. Kartsakalis, Angeliki Skoura, Vasileios Megalooikonomou


The fuzzy composition of objects depicted in images acquired through MR imaging or the use of bio-scanners has often been a point of controversy for field experts attempting to effectively delineate between the visualized objects. Modern approaches in medical image segmentation tend to consider fuzziness as a characteristic and inherent feature of the depicted object, instead of an undesirable trait. In this paper, a novel technique for efficient image retrieval in the context of images in which segmented objects are either crisp or fuzzily bounded is presented. Moreover, the proposed method is applied in the case of multiple, even conflicting, segmentations from field experts. Experimental results demonstrate the efficiency of the suggested method in retrieving similar objects from the aforementioned categories while taking into account the fuzzy nature of the depicted data.

Keywords: fuzzy object, fuzzy image segmentation, MRI imaging, object-based image retrieval, Motion Descriptors

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[1] J. K. Udupa, S. Samarasekera, “Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation.” Graphical Model and Image Processing, vol. 58, no. 3, pp. 246-261, May 1996.
[2] K. Ch. Ciesielski, J. K. Udupa, P. K. Saha, and Y. Zhuge, “Iterative Relative Fuzzy Connectedness for Multiple Objects with Multiple Seeds,” Computer Vision and Image Understanding, vol. 107, no. 3, pp. 160-182, Sep. 2007.
[3] J. K. Udupa, P. K. Saha, “Fuzzy Connectedness and Image Segmentation,” Proceedings of the IEEE, vol. 91, no. 10, pp. 1649-1669, Oct. 2003.
[4] J. K. Udupa, P. K. Saha, and R. A. Lotufo, “Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 11, pp. 1485-1500, Nov. 2002.
[5] R. Datta, D. Joshi, J. Li and J. Z. Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age”, ACM Computing Surveys, vol. 40, no. 2, pp. 1-60, Sep. 2008.
[6] M. J. Swain, D. H. Ballard “Color indexing”, International Journal of Computer Vision, vol. 7, no. 1, pp. 11-13, Nov. 1991.
[7] J. L. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack, “Efficient color histogram indexing for quadratic form distance functions”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 17, no. 7, pp. 729-736, July 1995.
[8] M. Stricker, and A. Dimai, “Color indexing with weak spatial constraints”, in Proc. SPIE Storage and Retrieval of Still Image and Video Databases IV, vol. 2670, 1996, pp. 29-40.
[9] J. Huang, S. R. Kumar, M. Mitra, W. Zhu, and R. Zabih, “Image indexing using color correlograms”, in Proc. Computer Vision and Pattern Recognition, IEEE Computer Society, Puerto Rico, 1997, pp. 762-768.
[10] R. C. Gonzalez and P. Wintz, Digital Image Processing, 2nd ed. Boston, MA: Addison-Welsey Longman Publishing Co., 1987.
[11] S. Li, M. Lee, and C. Pun, “Complex Zernike Moments Features for Shape-Based Image Retrieval,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 39, no. 1, pp. 227-237, Jan. 2009.
[12] F. Smach, C. Lemaitre, J. Gauthier, J. Miteran, and M. Atri, “Generalized Fourier Descriptors with Applications to Object Recognition in SVM Context,”, Journal of Mathematical Imaging and Vision, vol. 30, no. 1, pp. 43-71, Jan. 2008.
[13] H. Fonga, “Pattern Recognition in gray-level images by Fourier analysis”, Pattern Recognition Letters, vol. 17, no. 14, pp. 1477-1489, Dec. 1996.
[14] C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, “Fast Subsequence Matching n Time-Series Databases”, in Proc. of ACM SIGMOD international conference on Management of data, Minneapolis, 1994, pp. 419-429.
[15] K. M. Antshela, W. R. Katesa, N. Roizena, W. Fremonta and R. J. Shprintzena, “22q11.2 Deletion Syndrome: Genetics, Neuroanatomy and Cognitive / Behavioral Features Keywords”, Child Neuropsychology: A Journal on Normal and Abnormal Development in Childhood and Adolescence,”, vol. 11, issue 1, pp. 5-19, Feb. 2005.
[16] R. Agrawal, C. Faloutsos, and A. Swami, “Efficient similarity search in sequence databases,” in Proc. of 4th International Conference of Foundations of Data Organization and Algorithms (FODO), Chicago, 1993, pp. 69-84.
[17] A Machado, T. Simon, V. Nguyen, D. McDonald-McGinn, E. Zackai, and J. Gee, “Corpus callosum morphology and ventricular size in chromosome 22q11.2 deletion syndrome”, Brain Research, vol. 1131, pp. 197-210, Feb. 2007.