On wavelets applications in medical image denoising

• Autorzy: El-Henawy, I. , El-Areef, T. , Karawia, A. A.
• Języki publikacji: EN

Abstrakty

EN

An important application domain of the wavelet theory is denoising. In this paper, we use the wavelet transforms to denoise the medical images. There are many kinds of noise and we study only three types; i) additive random noise; ii) pop noise and; iii) localized random noise. Further, we use Root Mean Square Error(RMSE) and Signal to Noise Ratio (SNR) to measure the error between a noisy image and the original image.

Słowa kluczowe

EN

wavelet transform; noise modelling; image processing

• Czasopismo: Machine Graphics and Vision
• Rocznik: 2003
• Tom: Vol. 12, No. 3
• Strony: 393-404
• Opis fizyczny: Bibliogr. 14 poz., rys., tab

Twórcy

autor

El-Henawy, I.

• Faculty of Science, Mansoura University, Egypt,

autor

El-Areef, T.

• Ain Shams Universuty, Faculty of Computer and Information Sciences

autor

Karawia, A. A.

• Faculty of Science, Mansoura University, Egypt,

Bibliografia

• [1] Mallat S.: A theory for multiresolution signal decomposition: the wavelet representations. IEEE Trans. PAMI, 11, 674-693. 1989.
• [2] Chui C. K.: An Introduction to Wavelets. AP, San Diego, CA. 1992.
• [3] Daubechies I.: Ten Lectures on Wavelets. Philadelphia, PA: Soc. Ind. and Appl. Math. 1992.
• [4] Slezak E., Durret F., Gerbal D.: A wavelet analysis search for structures in eleven x-ray clusters of calaxied. Astrophysical Journal, 108(6), 1996-2008. 1994.
• [5] Kappeler C., Müller S. P.: Wavelet compression of noisy tomographic images. Proc. of the SPIE, 2569, 644-652. 1995.
• [6] Unser M., Aldroubi A.: A review of wavelets in biomedical applications. Proc. IEEE, 84, 626-638. 1996.
• [7] Aldroubi A., Unser M.: Wavelets in Medicine and Biology. CRC Press , Boca Raton FL, USA. 1996.
• [8] Starek J. L., Pierre M.: Structure detection in low intensity X-ray images. Astronomy and Astrophysics, Suppl. Ser., 128, 397-407. 1998.
• [9] Vidakovic B.: Statistical Modeling by Wavelets. John Wiley & Sons, Inc., New York. 1999.
• [10] Chang S. G., Yu Bin, Vetterli M.: Adaptive wavelet thresholding with context modeling for image denoisin. IEEE Trans. on IP, 9(9), 1522-1531. 2000.
• [11] Murtagh F., Starek J. L.: Image processing through multiscale analysis and measurement noise modeling. Statistics & Computing, 10, 95-103. 2000.
• [12] Oktem R.: Transform Domain Algorithms for Image Compression and Denoising. PhD thesis, Tampere University of Technology. 2000.
• [13] Pizurica A., Philips W., Lemahieu I., Acheroy M.: A joint inter- and intrascale statistical model for Bayesian wavelet bas ed image denoising. IEEE Trans. on IP, 11(5), 545-557. 2002.
• [14] Pizurica A., Philips W., Lemahieu I., Acheroy M.: A versatile wavelet domain noise filtration technique for medical imaging. IEEE, Trans. on Medical Imaging, 22(3), 323-331. 2003.