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Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

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Authors and Affiliations

  1. Faculty of Computers and Informatics, Department of Operations Research, Zagazig University, Zagazig, Egypt

    Mohamed Abdel-Basset

  2. Faculty of Information Systems and Computer Science, October 6 University Cairo, Cairo, Egypt

    Ahmed E. Fakhry

  3. Computer Science Department, Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt

    Ibrahim El-henawy

  4. School of Software, Dalian University of Technology, Dalian, 116620, China

    Tie Qiu

  5. School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, 632014, India

    Arun Kumar Sangaiah

Authors
  1. Mohamed Abdel-Basset
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  2. Ahmed E. Fakhry
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  3. Ibrahim El-henawy
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  4. Tie Qiu
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  5. Arun Kumar Sangaiah
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Corresponding author

Correspondence to Arun Kumar Sangaiah.

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The authors declared that we do not have any conflict of interest for this research work. This article does not contain any studies with human participants or animals performed by any of the authors.

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This article is part of the Topical Collection on Image and Signal Processing

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Abdel-Basset, M., Fakhry, A.E., El-henawy, I. et al. Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization. J Med Syst 41, 197 (2017). https://doi.org/10.1007/s10916-017-0846-9

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  • DOI: https://doi.org/10.1007/s10916-017-0846-9

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