OJMI  Vol.3 No.4 , December 2013
Planar Scintigraphic Images Denoising
Abstract: Scintigraphic images are generally affected by a Poisson type random noise which diminishes qualitatively and quantitatively the images. Restoration techniques aim to “find” an object from one (or several) degraded observation(s). The objective of the restoration is then to produce an image closer to the physical reality. So that the restoration is successful, it is very useful to know the nature of degradation. In this work, we present a planar scintigraphic acquisition chain modeling. This model takes into account the Poisson noise and its stationarity aspect. Then, we present a comparative study of the multi-resolution methods used to reduce the noise in scintigraphic images. Scintigraphy is a tool for exploring functionally several pathologies: the ventricular ejection fraction, the renal clearance and the thyroid activity. Given the fact that scintigraphic images are strongly affected by noise, the objective in this work is to enhance scintigraphic images for a reliable diagnosis and better orientation and understanding of the pathological phenomenon. This paper focuses on two main parts: the first deals with the degradation of model while the second takes into consideration the comparison of the multi-resolution methods for assessing the quality of scintigraphic images to reduce noise using wavelet, contourlet, curvelet, ridgelet and bandelet transformations.
Cite this paper: F. Makhlouf, H. Besbes, N. Khalifa, C. Ben Amar and B. Solaiman, "Planar Scintigraphic Images Denoising," Open Journal of Medical Imaging, Vol. 3 No. 4, 2013, pp. 116-124. doi: 10.4236/ojmi.2013.34019.

[1]   N. Khlifa, K. Hamrouni and E. Noureddine, “Image Denoising Using Wavelets: A Powerful Tool to Overcome Some Limitations in Nuclear Imaging,” 2nd International Conference on Information and Communication Tech- nologies, Vol. 1, 2006, pp. 1114-1118.

[2]   P. Rodríguez, “Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review,” Journal of Electrical and Computer Engineering, Vol. 2013, 2013, Article ID: 217021.

[3]   P. L. Combettes and H. J. Trussell, “Modèle et Algorithmes en vue de la Restauration Numérique d’Images Rayon X,” Cognitiva, 1987.

[4]   L. Comas, “Modèle et Algorithme pour la Scintigraphie Cardiaque,” Ph.D. Thesis, 2005.

[5]   T. S. Curry, J. E. Dowdey and R. C. Murry Jr., “Christensen’s Introduction to the Physics of Diagnostic Radiology,” 3rd Edition, Lea & Febiger, Philedelphia, 1984.

[6]   O. Villegas, H. Domínguez and V. Sánchez, “A Comparison of the Bandelet, Wavelet and Contourlet Transforms for Image Denoising,” Bibliometrics Data, 2008, pp. 207-212.

[7]   A. Khan and M. Singh, “Wavelet Transform Based Image Denoising Using Different Thresholding Methods,” 4th ICCEE, 2011.

[8]   M. Do and M. Vetterli, “Contourlets,” Proceedings of beyond Wavelets, Academic Press, New York, 2002, pp. 1-27.

[9]   M. N. Do and M. Vetterli, “The Finite Ridgelet Transform for Image Representation,” I.J. Image, Graphics and Signal Processing, Vol. 1, 2012, pp. 44-50.

[10]   C. Ramesh, “Nuclear Medicine Physics: The Basics,” Lippincott Williams & Wilkins, Philedelphia, 2004.

[11]   R. A. Powsner and E. R. Powsner, “Essentials of Nuclear Medicine Physics,” Blackwell Publishing Ltd., Hoboken, 2006.

[12]   J. J. Pedreoso de Lima, “Nuclear Medicine Physics,” Taylor and Francis Group, LLC, 2011.

[13]   S. R. Cherry, J. A. Sorenson and M. E. Phelps, “Physics in Nuclear Medicine,” Elsevier Saunders, Amsterdam, 2012.

[14]   J. A. Thie, “Nuclear Medicine Imaging, an Encyclopedic Dictionary,” Springer-Verlag, Berlin Heidelberg, 2012.

[15]   K. Schwochau, “Technetium: Chemistry and Radiopharmaceutical Applications,” WILEY-VCH, 2000.

[16]   I. Zolle, “Technetium-99m Pharmaceuticals: Preparation and Quality Control in Nuclear Medicine,” Springer, New York, 2007.

[17]   J. L. Moretti, P. Rigo, A. Bischof-Delaloye, R. Taillefer, R. Caillat-Vigneron and G. Karcher, “Imagerie Nucléaire Fonctionnelle,” Masson, 1991.

[18]   M. A. Masnadi-Shirazi, Z. Azimifar and M. H. Sadreddini, “A Comparative Study between Wavelet and Contourlet Transform Features for Textural Image Classification,” 3rd International Conference on Information and Communication Technologies, Damascus, 7-11 April 2008, pp. 1-5.

[19]   N. T. Binh and A. Khare, “Multilevel Threshold Based Image Denoising in Curvelet Domain,” Computer Science and Technology, Vol. 25, No. 3, 2010, pp. 632-640.

[20]   G. J. Reddy, T. J. Prasad and M. N. Prasad, “Fingerprint Image Denoising Using Curvelet Transform,” Engineering and Applied Sciences, Vol. 3, No. 3, 2008.

[21]   G. Chen and W. Zhu, “Image Denoising Using Neighbouring Contourlet Coefficients,” Proceedings of the 5th International Symposium on Neural Networks: Advances in Neural Networks, Vol. 5264, 2008, pp. 384-391.

[22]   X. M. Li, G. P. Yan and L. Chen, “A New Method of Image Denoise Using Contourlet Transform,” Intelligent Information Technology Application, Vol. 3, No. 1, 2010, pp. 25-30.

[23]   X. Zhang and X. Jing, “Image Denoising in Contourlet Domain Based on a Normal Inverse Gaussian Prior,” Digital Signal Processing, Vol. 20, No. 5, 2010, pp. 1439-1446.

[24]   G. Y. Chen and B. Kégl, “Image Denoising with Complex Ridgelets,” Elsevier, Amsterdam, 2006, pp. 1439-1446.

[25]   G. Chen and W. Zhu, “Image Denoising Using Neighbouring Contourlet Coefficients,” Engineering and Applied Sciences, Vol. 5264, 2008, pp. 384-391.

[26]   O. Osiris, V. Villegas, H. Jesús, O. Domínguez and V. Guadalupe, “A Comparison of the Bandelet, Wavelet and Contourlet Transforms for Image Denoising,” Bibliometrics Data, 2008, pp. 207-212.