A image and also in reducing noise. In this

A traditional way to remove noise
from image data is to employ spatial filters. In Spatial filtering the
processed value for the current pixel depends on both itself and surrounding
pixels. Spatial
filter are classified into non-linear and linear filters.Linear filters process
time-varying input signals to produce output signals, subject to constraint of
linearity whereas non-linear filters produces output signals, subject to
non-linearity especially in removal of certain types of noise that are not
additive. 

Median
filtering is a nonlinear process which is useful in
preserving edges in an image and also in reducing noise. In this filter each
pixel in image looks at its adjacent neighbor pixel to decide whether or not it
is representative of its surroundings and replace the pixel value with the
median of those values. In median filtering ,sorting all the pixel value within
the window size from the surrounding neighbor into numerical order and then
replace the pixel with median intensity value of the pixels within the windows.Adaptive median filter5 is an improved
version of median filter which works within a rectangular region(window) with
each output pixel contains the median value of window neighborhood around the
corresponding pixel in the input images. This filter is basically used to
smooth the non-repulsive noise from two-dimensional signals without blurring
edges and preserved images which makes, it suitable for enhancing mammogram
images.Frost filter 7 is an
exponentially weighted averaging filter which is used to eliminate the
quantum noise from the mammograms in which the
coefficient of variation is the ratio of the local standard deviation to the
local mean of the distorted image. Frost filter uses weighted sum of values
with ‘n’ window size to replace central pixel and this weighted factor directly
proportional to the difference between central and other pixels.Wavelet
transforms filtering have become increasingly
important which play an extremely crucial role in image processing since it
allow both time and frequency analysis simultaneously. Wavelet
transform decomposes the input image into four lower sub-bands with
approximation coefficients (LL1) and detail coefficients (HH1, HL1, and LH1). To
obtain the next level of wavelet coefficients, the sub-band LL1 is further
decomposed and sampled which results in two-level wavelet decomposition. In
this technique ,elimination of any of the undesired sub-band or their
combinations are done and then reconstructing the original image  using inverse wavelet transform. In our
experiments, we use Haar wavelet and eliminate HH, LH, and HL bands
individually for first- and second-level decomposition.Histogram equalization is a nonlinear
contrast enhancement which is basically used for enhancing the appearance of
images. 5Histogram equalization enhances
the contrast of images by transforming the values in an intensity image, or an
indexed image, in such a manner that the output image histogram approximately
matches a given histogram.(i.e.it give a linear trend to the cumulative
probability function associated to the image.)For images which contain local regions of
low contrast bright or dark regions, histogram equalization doesn’t work
effectively so a modified histogram equalization technique called Contrast Limited Adaptive Histogram Equalization can be used on such images
for better results which consider only small regions and based on their local cumulative distribution
function(cdf), performs contrast enhancement of those regions.6 The
amount of contrast enhancement for some intensity is directly proportional to
the slope of the CDF function at that intensity level. Hence contrast
enhancement can be limited by limiting the slope of the CDF. The slope of CDF
at a bin location is determined by the height of the histogram for that bin.
Therefore if we limit the height of the histogram to a certain level we can
limit the slope of the CDF and hence the amount of contrast enhancement. The CLAHE method
seeks to reduce the noise and edges-shadowing effect produced in homogeneous
areas and was originally developed for medical imaging. This method has been
used for enhancement to remove the noise and reduces the edge-shadowing effect
in the pre-processing of digital mammogram.

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