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Similar MRI object retrieval based on modified contour to centroid triangulation with arc difference rate

Authors:

Jeong K. PackAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 31 - 32

https://doi.org/10.1145/2554850.2555110

Published: 24 March 2014 Publication History

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Abstract

In this paper, we propose a new image retrieval method based on Sectored Contour to Centroid Triangulation (SCTCT) using distinctive shape feature, named Arc Difference Rate (ADR). We utilized Support Vector Machine (SVM) method as an extraction tool to extract suspicious tumor area as binary object image from the breast MRI. Therefore extracted 100 binary object images are used as test cases in the experimental study. The results from proposed method show the improvement in finding correct matches compare to the traditional SCTCT.

References

[1]

Attalla, Emad, and Pepe Siy. "Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching." Pattern Recognition 38.12, 2005: 2229--2241.

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[2]

CM Wang, XX Mai, GC Lin "Classification for Breast MRI Using Support Vector Machine,"" IEEE 8^th International Conference on Computer and Information Technology Workshops. 2008

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[3]

Jung, Byung K., "A sectored object matching approach for breast magnetic resonance image similarity study." Proceedings of the 2012 ACM Research in Applied Computation Symposium. ACM, 2012.

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[4]

Jung, Byung K., "Magnetic Resonance Image Retrieval Based on Contour to Centroid Triangulation with Shape Feature Similarity." Proceedings of the 2013 ACM Research in Applied Computation Symposium. ACM, 2013.

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[5]

Lehmann, Thomas M., et al. "Content-based image retrieval in medical applications for picture archiving and communication systems." Medical Imaging 2003. International Society for Optics and Photonics, 2003.

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Chiatti ACho MGagneja AYang XBrinberg MRoehrick KChoudhury SRam NReeves BGiles CHaddad HWainwright RChbeir R(2018)Text extraction and retrieval from smartphone screenshotsProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167236(948-955)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167236
Matsuda YOgawa MYano M(2015)Shape Retrieval With Geometrically Characterized Contour PartitionsIEEE Access10.1109/ACCESS.2015.24516273(1161-1178)Online publication date: 2015
https://doi.org/10.1109/ACCESS.2015.2451627

Index Terms

Similar MRI object retrieval based on modified contour to centroid triangulation with arc difference rate
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
      1. Image processing

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Published In

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
Jiman Hong
Soongsil University, South Korea

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 24 March 2014

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Ministry of Science, ICT and Future Planning

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SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

Gyeongju, Republic of Korea

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Chiatti ACho MGagneja AYang XBrinberg MRoehrick KChoudhury SRam NReeves BGiles CHaddad HWainwright RChbeir R(2018)Text extraction and retrieval from smartphone screenshotsProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167236(948-955)Online publication date: 9-Apr-2018
https://dl.acm.org/doi/10.1145/3167132.3167236
Matsuda YOgawa MYano M(2015)Shape Retrieval With Geometrically Characterized Contour PartitionsIEEE Access10.1109/ACCESS.2015.24516273(1161-1178)Online publication date: 2015
https://doi.org/10.1109/ACCESS.2015.2451627

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Magnetic resonance image retrieval based on contour to centroid triangulation with shape feature similarity

Similar Image Retrieval of Breast Masses on Ultrasonography Using Subjective Data and Multidimensional Scaling

A sectorized object matching approach for breast magnetic resonance image similarity study

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