Extraction of Semantic Digital Signatures from MRI Photos for Image-Identification Purposes
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Extraction of Semantic Digital Signatures from MRI Photos for Image-Identification Purposes

Authors: Marios Poulos, George Bokos

Abstract:

This paper makes an attempt to solve the problem of searching and retrieving of similar MRI photos via Internet services using morphological features which are sourced via the original image. This study is aiming to be considered as an additional tool of searching and retrieve methods. Until now the main way of the searching mechanism is based on the syntactic way using keywords. The technique it proposes aims to serve the new requirements of libraries. One of these is the development of computational tools for the control and preservation of the intellectual property of digital objects, and especially of digital images. For this purpose, this paper proposes the use of a serial number extracted by using a previously tested semantic properties method. This method, with its center being the multi-layers of a set of arithmetic points, assures the following two properties: the uniqueness of the final extracted number and the semantic dependence of this number on the image used as the method-s input. The major advantage of this method is that it can control the authentication of a published image or its partial modification to a reliable degree. Also, it acquires the better of the known Hash functions that the digital signature schemes use and produces alphanumeric strings for cases of authentication checking, and the degree of similarity between an unknown image and an original image.

Keywords: Computational Geometry, MRI photos, Image processing, pattern Recognition.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1062774

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References:


[1] K. Dalal, Counting the onion. Random Struct. Algorithms 24(2): 155- 165 (2004).
[2] M. Poulos, E. Magkos, V. Chrissikopoulos and N. Alexandris. "Secure Fingerprint Verification Based on Image Processing Segmentation using Computational Geometry Algorithms", Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, Rhodes Island, Greece, June 30- July 2, 2003, ACTA Press, 308-312.
[3] Lee, J.-W.; Khargonekar, P.P. " A convex optimization-based nonlinear filtering algorithm with applications to real-time sensing for patterned wafers" Automatic Control, IEEE Transactions on 48 (2), 224 - 235 (2003).
[4] http://doi.org/handbook_2000/metadata.html#4.3.3
[5] Shah B. and Raghavan V. and Dhatric P. and Zhao X. (2006). A Cluster- Based Approach for Efficient Content-Based Image Retrieval Using A Similarity-Preserving Space Transformation Method. Journal of the American Society for Information Science and Technology, 57, 1694-- 1707.
[6] Zachary J. and Iyengar SS and Barhen J. (2001). Content Based Image Retrieval and Information Theory: A General Approach. Journal of the American Society for Information Science and Technology, 52, 840-- 852.
[7] M. J. Atallah (1983). "A linear time algorithm for the Hausdorff distance between convex polygons". Information Processing Letters, v. 17, pp. 207-209.
[8] http://www.nlm.nih.gov/
[9] Marios Poulos, George Bokos, Fotios Vaioulis: Towards the semantic extraction of digital signatures for librarian image-identification purposes. JASIST 59(5): 708-718 (2008).
[10] Poulos M., Papavlasopoulos S. and Chrissikopoulos V. (2004). A text categorization technique based on a numerical conversion of a symbolic expression and an onion layers algorithm.Journal of Digital Information, Vol 6, Is. 1.