Measurement of one of the filtered images being compared,

Measurement of image quality is very important to many
image processing systems. Since it inherent physical limitations and economic
reasons for the quality of images and videos, such that it could view by a
human observer. Some of existing measures of image quality are listed below.Image
differencing is used to determine changes between images and Maximum
difference (MD) is the
maximum of the error between real image and filtered image. For the better
performance maximum difference should be minimum and large value shows poor
quality.The root mean square error (RMSE) is the square root of cumulative
squared error between the filtered and the original image for comparing various
image processing. For the
better performance root mean square
error should be minimum and large value shows poor qualityThe peak signal-to-noise ratio(PSNR) in
image processing is a standard measure of the sensitivity of
imaging system. For the better
performance peak signal-to-noise
ratio should be maximum and smaller value shows poor quality.In image processing normalized absolute error (NAE)
is a measure of difference between the
filtered and the original image for comparing various images. Higher value of
normalized absolute error is used for poor quality of images.Smaller the value of structural content (SC) better is the
image quality and higher value of structural content is used for poor quality
of images.For image-processing
applications in which the brightness of the image and template can vary due to
lighting and exposure conditions, the images can be first normalized. Normalized Cross Correlation (NCC) show
better quality with higher value.Laplacian
mean squared error (LMSE) is based on the importance of edges
measurement with lower the value of laplacian mean squared error better the
quality of images.Universal
image quality index (IQI) is a image quality distortion measurement which is defined
as the product of three factors: structural distortion, contrast distortion and
luminance distortion, of the distortion of ideal image with respect to filtered
image.The Structural Similarity (SSIM) index is a method for measure
similarity between ideal image and filtered image. It is an quality measure of
one of the filtered images being compared, provided
the ideal image is regarded as of perfect quality . SSIM is a better than that
of universal image quality index.  The Pratt’s Figure of Merit(PRATT) find the edge location accuracy by
the displacement of filtered image from an ideal image where both the image
(filtered & ideal) are edge detected images from the edge detectors like
sobel , prewitt , Robert , log etc..

The Mammographic Image Analysis Society (MIAS)
database is the largest publicly available database of mammographic data. It
contains approximately 322 screening mammography cases, where 207 images
represent normal, while 64 and 51 images referred as benign and malignant cases
respectively.

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For experimental purpose, the 45 images are taken from
MIAS database which includes of 15 normal images and 30 abnormal images. The
abnormal images are again classified into two classes which are benign and
malign. There are 15 benign images and 15 malign images.

In this section, the results of those filters are compared which are
discussed in previous section. Many proposed work in the literature are also
discussed and compared with the present approach. Furthermore, the filtered
images shown in this section are obtained as a result of subjecting the breast
mammogram shown in Figure1 to various
filters.The image quality metrics of median filter
are presented in Table 1 and the results of median filter applied to the
original mammogram of Figure1 are shown in Figure 2 . From the image quality metrics table it is
found that, the image quality degrades with the increase in windows size. Some
of the performance of image metrics (MD,NAE,LMSE,RMSE,SC) increase and some
(PSNR,IQI, SSMI,NCC) decreases with the increase of windows size .Also pratts
figure of merit show better performance with lower window size.Table
2 shows the image quality metrics obtained for Adaptive
median filter (AMF) and Figure 3 shows the results for AMF. In AMF the image
quality will not change much with the increase in windows size .It is found
that performance of AMF is quite good resulting in lower value of RMSE and
higher value of PSNR. The Pratts figure of merit also shows better results as compared to
median filter.Figure 4 shows the result of frost filter
and the image quality metrics obtained for frost filter is presented in Table 3. Here the
image quality will change variably with the increase in windows size .It is
found that performance of frost filter is quite good resulting in window size
5.Figure 5 shows the filtered images from wavelet
filtering, and the corresponding tables of image quality metrics are presented
in Table 4. It is found that image quality is
maintained after filtering at first-level decomposition as indicated by RMSE,
PSNR, IQI and SSMI while after second-level decomposition, image becomes much
brighter and Pratt’s figure of merit reduces. When LH band is eliminated after
second-level decomposition, most of the details are lost giving MD of 0.000 and
NAE of 254.00 .The best result is obtained when HH band is eliminated after
first-level decomposition.The image quality metrics
obtained for histogram equalization(column 2) & CLAHE(column 3) is presented
in Table 5 and the results for histogram
equalization (a)& CLAHE (b)are shown in figure 6. It is found that with
maximum difference and psnr in histogram equalization is smaller than all other
filter.In this paper, review and comparison of representative denoising
methods both qualitatively and quantitatively with extensive experiments
conduct to evaluate the performance of all the algorithms. In analytical
comparison, it was found that image representations with over complete basis
functions improve the performance within each category.In this paper it is clear from the comparison that all the
denoising techniques are important for various applications. In applications
that require high efficiency, some filters are used, some filters are more
appropriate for high searching complexity, 
memory and complexity issue .