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A New Approach for Contrast Enhancement in SPECT Imaging based on Gradient Approximation and Histogram Equalization (GAHE)
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Nuclear medicine is playing a major role in medical diagnosis. But, due to limitations in both injected radionuclide to the patient’s body and low count rates of the detector, output images are of very low contrast. Several methods have been proposed to improve the contrast of medical images. In this study, a new method is presented for SPECT images. The proposed method is based on the combination of Gradient Approximation (GA) and Histogram Equalization (HE) algorithms to improve the image contrast. Poisson editing concept is deployed to allow the images to be edited and processed in the gradient domain before the reconstruction phase. GA is initially applied on the images to overcome the limitations of HE method. Using the GA concept, image gradients are manipulated first and then the images are reconstructed. These reconstructed images are fed as input for the HE block. Finally, results are presented both qualitatively and quantitatively.
Keywords
SPECT, Contrast Enhancement, Histogram Equalization, Gradient Approximation.
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- National Research Council (US) and Institute of Medicine (US) Committee on the Mathematics and Physics of Emerging Dynamic Biomedical Imaging, “Single Photon Emission Computed Tomography”, Mathematics and Physics of Emerging Biomedical Imaging, National Academies Press, 1996.
- M. Agarwal and R. Mahajan, “Medical Image Contrast Enhancement using Range Limited Weighted Histogram Equalization”, Procedia Computer Science, Vol. 125, pp. 149-156, 2018.
- H. Kaur and J. Rani, “MRI Brain Image Enhancement using Histogram Equalization Techniques”, Proceedings of IEEE International Conference on Wireless Communications, Signal Processing and Networking, pp. 770-773, 2016.
- F. Mirzaei and R. Faghihi, “Quantification of Contrast Agent Materials using A New Image Domain Multi Material Decomposition Algorithm based on Dual Energy CT”, British Institute of Radiology, Vol. 1, No. 1, p. 20180008, 2019.
- F. Mirzaei, M. Faridafshin, A. Movafeghi and R. Faghihi, “Automated Defect Detection of Weldments and Castings using Canny, Sobel and Gaussian filter Edge Detectors: A Comparison Study”, Proceedings of IEEE International Conference on Non-Destructive Testing, pp. 101-108, 2017.
- F. Mirzaei, M.R. Parishan, M. Faridafshin, R. Faghihi and S. Sina, “Automated Brain Tumor Segmentation in MR Images using A Hidden Markov Classifier Framework Trained by SVD-Derived Features”, ICTACT Journal on Image and Video Processing, Vol. 9, No. 1, pp. 1844-1848, 2018.
- R. Maini and H. Aggarwal, “A Comprehensive Review of Image Enhancement Techniques”, Journal of Computing, Vol. 2, No. 3, pp. 8-13, 2010.
- S.S. Bedi and R. Khandelwal, “Various Image Enhancement Techniques A Critical Review”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, No. 3, pp. 1605-1609, 2013.
- A.B. Patrick Perez, Michel Gangnet and Andrew Blake, “Poisson Image Editing”, ACM Transactions on Graphics, Vol. 22, No. 3, pp. 313-318, 2003.
- P.J. Hampton and P. Agathoklis, “Comparison of HAAR Wavelet-Based and Poisson-Based Numerical Integration Techniques”, Proceedings of IEEE International Symposium on Circuits Systems, Nano Circuits Fabrication Systems, pp. 1623-1626, 2010.
- I.S. Sevcenco, P.J. Hampton and P. Agathoklis, “A Wavelet Based Method for Image Reconstruction from Gradient Data with Applications”, Multidimensional Systems and Signal Processing, Vol. 26, No. 3, pp. 717-737, 2015.
- P. Bhat, B. Curless, M. Cohen and C.L. Zitnick, “Fourier Analysis of the 2D Screened Poisson Equation for Gradient Domain Problems”, Lecture Notes in Computer Science, Springer, Vol. 5303, No. 2, pp. 114-128, 2008.
- L. Huang, W. Zhao, Z. Sun and J. Wang, “An Advanced Gradient Histogram and its Application for Contrast and Gradient Enhancement”, Journal of Visual Communication and Image Representation, Vol. 31, pp. 86-100, 2015.
- J.M. Morel, A.B. Petro and C. Sbert, “Fourier Implementation of Poisson Image Editing”, Pattern Recognition Letters, Vol. 33, No. 3, pp. 342-348, 2012.
- T. Shibata, M. Tanaka and M. Okutomi, “Gradient-Domain Image Reconstruction Framework with Intensity-Range and Base-Structure Constraints”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2745-2753, 2016, pp. 2745–2753.
- A. Kumar Pandey, P.D. Sharma, P. Dheer, G.K. Parida, H. Goyal, C. Patel, C. Bal and R. Kumar, “Investigating the Role of Global Histogram Equalization Technique for 99m Technetium‑Methylene diphosphonate Bone Scan Image Enhancement”, Indian Journal of Nuclear Medicine, Vol. 33, No. 1, pp. 1-5, 2018.
- Y.M.Y. Abdallah and E. Wagiallah, “Enhancement of Nuclear Medicine Images using Filtering Technique”, International Journal of Science and Research, Vol. 3, No. 8, pp. 916-921, 2014.
- D. Asamoah, E. Ofori, S. Opoku and J. Danso, “Measuring the Performance of Image Contrast Enhancement Technique”, International Journal of Computer Applications, Vol. 181, No. 22, pp. 6-13, 2018.
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