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Image Reconstruction Scheme Based on Phase Correction and Singularity Function Analysis Model

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Abstract

The singularity function analysis (SFA) model is a mathematical tool that allows representing an image with much less coefficients than the conventional Fourier transform (FT) model while maintaining a better image quality. The performance of the SFA method for reconstructing medical images may however degrade if unwanted phase drifts are present in the acquired image. This paper proposes a new SFA-based reconstruction scheme by taking into account phase drifts. To this end, phase drifts are first mathematically formulated and corrected. The singularity function model is then applied to represent the phase-corrected image. The performance of this phase-corrected SFA reconstruction scheme is evaluated using both simulated and real brain images, and compared with the conventional FT model and SFA method without phase correction. The results demonstrate that the proposed reconstruction method achieves significant improvement in image quality.

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Acknowledgment

This work was partly supported by the NSF of China under 30670574, the project Mira Recherche 2005 of the Region Rhône-Alpes of France, the project Arcus Chine 2005 of French Ministry of Foreign Affairs, Shanghai International Cooperation Grant under 06SR07109, and High Technology Research Development Plan (863 plan) of P. R. China under 2006AA020805.

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Authors and Affiliations

  1. College of Life Science and Technology, Shanghai Jiaotong University, 200240, Shanghai, People’s Republic of China

    Jianhua H. Luo & Huan Luo

  2. Creatis-Lrmn, CNRS UMR 5220, Inserm U 630, University of Lyon 1, INSA of Lyon, Villeurbanne, France

    Yuemin M. Zhu

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  1. Jianhua H. Luo
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  2. Huan Luo
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  3. Yuemin M. Zhu
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Correspondence to Jianhua H. Luo.

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Luo, J.H., Luo, H. & Zhu, Y.M. Image Reconstruction Scheme Based on Phase Correction and Singularity Function Analysis Model. J Sign Process Syst Sign Image Video Technol 54, 79–88 (2009). https://doi.org/10.1007/s11265-008-0190-x

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  • DOI: https://doi.org/10.1007/s11265-008-0190-x

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