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On The Denoising Of Nuclear Medicine Chest Region Images

Sozopol 2004. On The Denoising Of Nuclear Medicine Chest Region Images. Faculty of Technical Sciences Bitola, Macedonia. Cvetko D. Mitrovski, Mitko B. Kostov. Structure. Aim / Problem formulation NM images creation process Wavelet shrinkage The filtration of images Experimental results

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On The Denoising Of Nuclear Medicine Chest Region Images

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  1. Sozopol 2004 On The Denoising Of Nuclear Medicine Chest Region Images Faculty of Technical Sciences Bitola, Macedonia Cvetko D. Mitrovski, Mitko B. Kostov

  2. Structure • Aim / Problem formulation • NM images creation process • Wavelet shrinkage • The filtration of images • Experimental results • Conclusion

  3. Aim of the Work & Problem Formulation • AIM: To develop methods for analyzing of anatomical data and ROIs on a basis of a raw NM image (set of raw NM images). • PROBLEM: To find a suitable method for automatic preprocessing of the chest region NM images & extraction of the anatomical data.

  4. NM Images Creation Process • The raw NM images are based directly on the total counts • a low signal-to-noise ratio (SNR) • noisy due to low count levels, scatter, attenuation, and electronic noises in the detector/camera • One of the major sources of error is Poisson noise due to the quantum nature of the photon detection process

  5. Wavelet Shrinkage Program • DWT (produces two groups of coefficients with low and high SNR) • = wihi • hihard = • hisoft = • Inverse wavelet transformation

  6. Filtration of Chest Region Images • Wavelet shrinkage (threshold for Poisson model?) • the Anscombe variance-stabilizing transformation: • the Donoho’s level dependent threshold: • give up the perfect reconstruction (QMF bank – near PR) Poisson  Gaussian noise model

  7. The Algorithm • transformation of the image • calculation of Donoho’s threshold (s =MAD/0.6745) MAD is the median of the magnitudes of all the coefficients at the finest decomposition scale • wavelet soft-thresholding • inverse wavelet transform • square the result • removing shadow in the obtained image

  8. Experimental Results

  9. The QMF Bank QMF bank has overall reconstruction error minimized in the minimax sense; the corresponding QMF filters have least-squares stopband error

  10. Comparison with biorthogonal wavelets

  11. Comparison with Daubechies with Symlets

  12. The QMF Bank with meyer

  13. Conclusions • The presented method offers automatic extracting of the anatomic data from the chest region NM images • The method involves: DWT shrinkage program, variance-stabilizing transformation, QMF filters • Further analyzing of processed data (possible inequality between left and right side)

  14. Questions and discussion Thank you for your attention

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