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with more reliability. Bior4.4-wavelet is reliable at low levels of the decomposi-
tion, while bior1.1 or bior1.3 are more reliable at higher levels of decomposition
(higher CR). The best performance is shown by reverse biorthogonal wavelet—
rbio1.3 and Symlet of 2 order (sym2).
6.5 Conclusions
Total number of patients is 215. However, number of images are considerable
grater. Except original images, the most important part was to analyze recon-
structed images. For every patient, analyzed images were also reconstructions
from every level till 14th and for every type of wavelets (different moments, which
means different member of the family).
Basic contributions of the work are in determined facts:
• symlets can be used for up to do 14th level of decomposition and obtained
CR = 1316;
• biorthogonal wavelets cannot be used always. Their reliability is till 10th level
of decomposition and CR = 131.65;
• reverse biorthogonal wavelets can be used till 14th level of decomposition,
because there are of medical value when asbestosis is considered,
It is important to point out that data presented is medically valid, because the
independency restriction posed by ILO. Algorithm produce 100 % correct images
if mentioned wavelets are used for determined levels of decomposition.
Successful compression of pulmonary X-rays is obtained in the research by
wavelet transform. Maximal obtained CR was 1316, which means reduction of
disk space from 4.5 MB to 3.5 KB.
Tests and experimental work was performed in Matlab. However, system can
be automatized by specifying input/output drivers and exporting programing code
as C++ project and/or exe-file.
References
1. Pavlidis, T., Steiglitz, K.: The automatic counting of asbestos fibers in air samples. IEEE
Trans. Comput. C-27(3), 258-261 (1978)
2. Paustenbach, D.J.: Bhopal, asbestos, and Love Canal… how they should affect engineering
education. IEEE Technol. Soc. Mag. 6(1), 9-15 (1987)
3. Petja, B.M., Twumasi, Y.A., Tengbeh, G.T.: The use of remote sensing to detect asbestos
mining
degradation
in
Mafefe
and
Mathabatha,
South
Africa.
In:
IEEE
International
Conference on Geoscience and Remote Sensing, pp. 1591-1593 (2006)
4. Petja, B.M., Twumasi, Y.A., Tengbeh, G.T.: Comparative analysis of reflectance
spectroscopy and laboratory based assessment of asbestos pollution in the rehabilitated
mining environment, South Africa. In: IEEE International Geoscience and Remote Sensing
Symposium, pp. 1246-1249 (2007)
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