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.