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45I15 IJAET0715656 v6 iss3 1424to1430.pdf

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International Journal of Advances in Engineering &amp; Technology, July 2013.
ISSN: 22311963
automatic estimation of the crucial parameters was not addressed [9]. A modified diffusion scheme,
suitable for images with low-contrast and uneven illumination, was described in [10].
Qasima Abbas Kazmi et al. (2013) The given Approach is to generalize the diffusion process further
into forward-and-backward process. Further the Forward - and Backward diffusion process could
again be used in Enhancement of the resolution of the given image. A single image is being used for
enhancement of resolution of that image by using interpolation and a forward-and-backward nonlinear
diffusion post-processing provides suppression of ringing. Process is found to be very productive in
distinguishing those medical images which gives similar images for two or more dangerous diseases.
The process respects the boundaries between the edges [11].



Anisotropic diffusion filtering schemes based on nonlinear diffusion, developed for image
enhancement. Since first proposed by Perona and Malik in 1990 [12], anisotropic diffusion has been
developed and applied to different areas of image processing. To avoid blurring at the edges, instead
of using the constant diffusion coefficients based on the original linear anisotropic diffusion, an edge
stopping function was proposed to estimate the diffusion coefficients, which ensures the diffusion
process taking place mainly inside of the regions rather than at their boundaries and thus the
smoothing happens only in the interior of regions without crossing the edges.
The main target of an efficient adaptive anistropic diffusion algorithms in medical image processing is
to remove noise via exponential diffusion function based on proposed a new edge –stopping function
as shown in the figure (1), In the method, an anisotropic coefficient kmp is used to stop the diffusion
over the edges of the image, it is called &quot;new edge-stopping function&quot;. The efficient adaptive
Anisotropic Diffusion Algorithm is:

Algorithm: a new adaptive Anisotropic Diffusion

As we are interested in enhancing a noise image, we want to find values for k over the boundaries of
every neighboring pixel that, after the application of the evolution equation, preserve image
boundaries and eliminate as much noise as possible, therefore, in our case k = k(Δ t). With this in
mind, an alternative considering only two possible values of k was studied, so k, s € {0 , 1}. Figure (1)
below illustrates the main steps of the proposed an efficient approach for adaptive anisotropic
diffusion for enhancing medical image.


Vol. 6, Issue 3, pp. 1424-1430