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A Novel Noise Removal Method for Lung CT SCAN Images Using Statistical Filtering Techniques

S Sivakumar, C Chandrasekar

Abstract


Image denoising could be a procedure in digital image process aiming at the removal of noise, which can corrupt a picture throughout its acquisition or transmission whereas retentive its quality. Medical image sweetening technologies have attracted a lot of attention since advanced medical instrumentation were place into use within the medical field. Increased medical pictures area unit desired by an operating surgeon to help designation and interpretation as a result of medical image qualities area unit usually deteriorated by noise and different information acquisition devices, illumination conditions, etc. Our targets for medical image sweetening area unit is principally to resolve issues of the high-level noise of a medical image. The noise gift within the pictures could seem as additive or increasing elements and also the main purpose of denoising is to get rid of these creaking elements whereas protective the vital signal the maximum amount as doable. During this paper we have a tendency to analyze the denoising filters like Mean, Median, Midpoint, Wiener filters and also the 3 additional changed filter approaches for the respiratory organ CT scan pictures to get rid of the noise gift within the pictures and compared by the standard parameters.

Keywords: Medical images, noise removal, filtering approach, statistical filters, quality measures

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References


Shepp L.A., Logan B.F. The Fourier reconstruction of a head section. IEEE Trans. Nucl. Sci. NS-21. 1974; 21–43p.

Huesman R.H. Analysis of statistical errors for transverse section reconstruction; Lawrence Berkeley Laboratory report #4278. University of California, Berkeley, Calif. 1975

Hanson K.M., Boyd D.P. The characteristics of computed tomographic reconstruction noise and their effect on detectability. IEEE Trans. Nucl. Sci. NS-25. 1978; 160–73p.

Ter-Pogossian M.M. The physical aspects of diagnostic radiology, New York: Harper & Row, Publishers. 1967.

Sivakumar S., Chandrasekar C. Lung Nodule Detection using Fuzzy Clustering and Support Vector Machines. IJETCAS. 2013; 14–117: 86–91p.

Cohen G. Contrast-detail-dose analysis of six different computed tomographic scanners. J. Comput. Assist. Tomogr. 1979; 3:197–203p.

Pullan B.R., Fawcitt R.A., Isherwood I. Tissue characterization by an analysis of the distribution of attenuation values in computed tomography scans: a preliminary report. J. Comput. Assist. Tomogr. 1978; 2: 49–54p.

Burgess A.E., Humphrey K., Wagner R.F. Detection of bars and discs in quantum noise. Proc. SPIE Appl. Opt. Instr. In Medicine.1979; 173(7): 34–40p.

Sivakumar S., Chandrasekar C. Lung Nodule Detection using Fuzzy Clustering and Support Vector Machines. IJET. 2013; 5(1): 179–85p.

Gonzalez R.C. Woods, Digital Image Processing. Englewood Cliffs; NJ: Prentice-Hall. 2002.

Jain A.K. Fundamentals of Digital Image Processing. Englewood Cliffs; NJ: Prentice-Hall. 1989.

Chen J., Benesty J., Huang Y. New Insights into the Noise Reduction Wiener Filter. IEEE Transactions on audio, speech, and language processing. 2006; 14(4).

Wang Z., Bovik A.C. A universal image quality index. IEEE Signal Processing Letters. 2002; 9(3): 81–4p.

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