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Home»Articles»A Novel Hybrid Framework for Medical Image Retrieval

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Asian Journal of Engineering and Applied Technology (AJEAT)

Editor Dr. Seshadri Ramkumar
Print ISSN : 2249-068X
Frequency : Quarterly

A Novel Hybrid Framework for Medical Image Retrieval

Author : A. Saravanan , M. Natarajan and S. Sathiamoorthy
Volume 7 No.2 July-December 2018 pp 37-41

Abstract

A new hybrid framework for Content-Based Medical Image Retrieval (MCBIR) is proposed in this paper to deals with the accuracy issues related with the existing MCBIR. The proposed hybrid framework initially divides the images into number of non-overlapping rectangular regions. Subsequently, statistical based color autocorrelogram (CA) and texture autocorrelogram (TA) is extracted for each region respectively. Then the geometric based chordiogram descriptor (CD) is extracted for each region. Both the statistical based and geometric based descriptors are combined to create a feature vector. The corresponding image regionsor patches in the query and target medical images are compared using the Canberra distance measure. The proposed hybrid framework is evaluated using the benchmark database and it is confirmed that it significantly outperforms the state-of-the-art system in terms precision, recall and G-measure.

Keywords

Color autocorrelogram, texture autocorrelogram, chordiogram descriptor

Full Text:

References

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Asian Journal of Engineering and Applied Technology is a peer-reviewed International research journal aiming at promoting and publishing original high quality research in all disciplines of engineering and applied technology. All research articles submitted to AJEAT should be original in nature, never previously published in any journal or presented in a conference or undergoing such process across the world. All the submissions will be peer-reviewed by the panel of experts associated with particular field. Submitted papers should meet the internationally accepted criteria and manuscripts should follow the style of the journal for the purpose of both reviewing and editing.

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A new hybrid framework for Content-Based Medical Image Retrieval (MCBIR) is proposed in this paper to deals with the accuracy issues related with the existing MCBIR. The proposed hybrid framework initially divides the images into number of non-overlapping rectangular regions. Subsequently, statistical based color autocorrelogram (CA) and texture autocorrelogram (TA) is extracted for each region respectively. Then the geometric based chordiogram descriptor (CD) is extracted for each region. Both the statistical based and geometric based descriptors are combined to create a feature vector. The corresponding image regionsor patches in the query and target medical images are compared using the Canberra distance measure. The proposed hybrid framework is evaluated using the benchmark database and it is confirmed that it significantly outperforms the state-of-the-art system in terms precision, recall and G-measure.

Editor-in-Chief
Dr. Seshadri Ramkumar
Department of Environmental Toxicology, Texas Tech University, Texas
[email protected]
Editorial Advisory Board
Dr. Kamarul Ariffin Bin Noordin
Department of Electrical Engineering, University of Malaya, Malaysia
[email protected]
Dr. Benjamin T.F. Chung
Department of Mechanical Engineering, University of Akron, Akron, USA
[email protected]
Dr. Mohd Faiz Bin Mohd Salleh
Department of Electrical Engineering, University of Malaya, Malaysia
[email protected]
Dr. Suhana Binti Mohd Said
Department of Electrical Engineering, University of Malaya, Malaysia
[email protected]
Dr. Norrima Binti Mokhtar
Department of Electrical Engineering, University of Malaya, Malaysia
[email protected]
Dr. Mohamadariff
Department of Electrical Engineering, University of Malaya, Malaysia
[email protected]

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    Editorial Note

    Editorial Dr. Seshadri Ramkumar

    Editor-in-Chief
    Dr. Seshadri Ramkumar
    Department of Environmental Toxicology, Texas Tech University, Texas
    [email protected]
    Editorial Advisory Board
    Dr. Kamarul Ariffin Bin Noordin
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Benjamin T.F. Chung
    Department of Mechanical Engineering, University of Akron, Akron, USA
    [email protected]
    Dr. Mohd Faiz Bin Mohd Salleh
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Suhana Binti Mohd Said
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Norrima Binti Mokhtar
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Mohamadariff
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]

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