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\title{Modified Multi-Wavelet Noise Filtering Algorithm for Mammographic Image Denoising using Recurrent Neural Network}
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\begin{document}

             \author[1]{Swapnil  Tamrakar}

             \author[2]{Abha  Choubey}

             \author[3]{Siddhartha  Choubey}

             \affil[1]{  C.S.V.T.U.}

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\date{\small \em Received: 12 April 2015 Accepted: 2 May 2015 Published: 15 May 2015}

\maketitle


\begin{abstract}
        


The digital mammographic images are affected by several types of noises which require filters to denoise the noise level. This will help the medical practitioner to enhance the image quality of the mammograms and helps them in giving accurate diagnosis. There are so many works on image denoising technique but there are not much which gives emphasis on the mammographic images. . In application point of view medical images are classified as Multispectral Image (used for satellite surveillance), RGB standard colour scheme Image or other digital versions of the film image i.e., in our case its mammographic image. For every image type it requires different approach for denoising because in each type of image, it contains different factors in it. In denoising the mammographic image , the filtering technique that is to be applied depend on its noises at each resolution level of the microns to make the micro-classification of the cancerous tissues to that of the bright water dense patches caused by the calcium salts in the mammary glands. Thus, any single algorithm cannot provide similar performance range for different types of noise because not every method is effective for the scenario of mammographic image denoising. In the given study we have shown a method for the mammographic image denoising which is having higher accuracy and the performance range is suited for denoising applications.raphic image denoising

\end{abstract}


\keywords{digital mammography, denoising, independent component analysis, wavelet shrinkage.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
he disease called Breast cancer is the most common cancer for the women of 35-55 age groups and is the cause of cancer death. In United States more than two million women are diagnosed with breast cancer treatment. The major cause of the disease breast cancer is still unknown hence the prevention is impossible. X-ray mammography imaging technique is used in breast cancer detection. Important sign of breast cancers is microclassification of clusters \hyperref[b0]{[1]}. In mammograms this microclassifications is seen as nodular points which are of high intensity localized diffusively along the breast and with high contrast.\par
There is a significant challenge in detecting early signs of breast cancers that is seen on X-ray mammograms because of the major influence of several Author ? ? ?: Department of Computer Science, Shri Shankracharya Technical campus (SSGI), Bhilai, Chhattisgarh. e-mails: swapnil.tamrakar33@gmail.com, abha.is.shukla@gmail.com, siddhartha00@gmail.com types of noises dictating the appearance of the final mammogram .The source of these noises may be from origin of malfunctioning equipment or from the faulty practices in recording the imagery data. The problem related to identifying and detecting the breast area which is inflamed with cancer virus, becomes sever with the naked eyes. This makes essential regions invisible or mixed with the noises.\par
From the past decades it has been observed that several denoising techniques for mammographic image denoising gives poor performance, fail to preserve the features of the image after denoising. Since number of methods are made of range of combination of fuzzy logic, wavelet transformation or of neural network \hyperref[b1]{[2]}\hyperref[b2]{[3]}\hyperref[b3]{[4]}\hyperref[b4]{[5]}\hyperref[b11]{12]}. So the mammograms shows varying contrast and brightness and hence the information is susceptible to being correlated \hyperref[b6]{[6]}\hyperref[b7]{[7]} {\ref [8]}\hyperref[b8]{[9]}. Some researchers used wavelet transformation where it tends to provide more consolidated results than the other methods \hyperref[b9]{[10]}\hyperref[b10]{[11]}; thus, the following study gives an effectively modelled algorithm for denoising the noisy mammographic images using multi-wavelet transformation and this will allow easy microclassification which will help radiologist to detect breast cancer easily. 
\section[{II.}]{II.} 
\section[{Methedology a) Experimental Setup}]{Methedology a) Experimental Setup}\par
The proposed model is implemented using MATLAB R2012a under Windows platform. The experiments are conducted over the machine with hardware configurations of Intel's third generation 8-core microprocessor with Nvidea 630 graphic card, 2GB RAM giving a fine clocking speed of 2.7 GHz. The consolidated database used in the study is DDSM (Digital Database for Screening Mammography) by the University of South Florida and is available online at \hyperref[b12]{[13]}. The images used in the study consist of three types and are classified into three types based on the amount of cancer influenced the mammary tissue. The mammographic consists of 16 bits of information and with the resolution ranging from 42-43.5 microns. The images are extracted by the scanners namely DBA, HOWTEK, \& LUMSIYS. These canners are deviated based on the optical density required to extract the information from the mammogram images. The properties of the used images are represented in the table 1 below.  
\section[{b) The Multi-Wavelet Wavelet Noise Filtering Model}]{b) The Multi-Wavelet Wavelet Noise Filtering Model}\par
We employ the multi-wavelet transformation to breakdown the given noisy image into a pyramid of features which is linked to one another in logical manner. This will allow us to perform tree based searching \& allocation for a given colour scheme which will be independent from feature decomposition for both high and low resolution image. Therefore, the image can be broken into wavelets by using the following functions as given below:ð??"ð??"(??, ??) = 1 ????? ? ? ?? ?? (?? ?? , ??, ??) ?? ?? ?? ?? ,?? ,?? (??, ??) ?? + 1 ????? ? ? ?? ð??"ð??" ?? (??, ??, ??) ?? ?? ð??"ð??" ?? ,?? ,?? ?? (??, ??)\par
Where, the indices ?? ?? , ??, ??, ?? are the nonnegative integers, x \& y are the pixels position at point P, M\&N are the real valued tensor coefficients, ?? is the scaling function and ð??"ð??" is the wavelet function in corresponding scaling and wavelet function is given by ?? ?? , ?? ð??"ð??" ?? . The scaling coefficients from the given noisy image are at different resolution in a mammogram while the wavelet coefficients from the feature vector in the noise retrieval step; that's the reason why different types of scanners are used in recording the mammogram which in turn is dependent on the optical density.\par
During the sampling phase the wavelet coefficients can be transformed into feature sets with the generalized association rule by formulating a Gaussian kernel based on the similarity of the coefficients and its characteristics from the given noisy image. The Gaussian kernel so formed s given as:?????, ?? ? ? = 1 ?? ?? ? |?? ?? (?? ? 1, ?? ? 1) ? ?? ?? (?? + 1, ?? + 1)| ??,????? ?? ?? ?? ???????? 2 2?? ?\par
Where, ?? ?? is the total number of neighbouring pixels in the spatial region of the pixel position x,y. Here ?? ?? is the regularized threshold value, ?? ?? is the intensity of the pixel value for the diagonal of the pixel region, ?? is characterised by gradient descent of the standard deviation for a particular band at different scales of the mammogram and x' is the next pixel position. Here, the emphasis is towards evaluating the kernel and updating it by pairing the formulation in association with one another. The flow chart of the work flow process involving the denoising process is given in the figure  {\ref 5} below. The threshold value of the wavelet to give a denoised image is determined as:     We have presented the quantifying success of the proposed algorithm against the three mostly used techniques for denoising the digital mammographic images. The above table 1 represents the performance range of mammographic images with different amount of noise influence represented in form of noise percentage for a DDSM database. The figure  {\ref 6} shows some of the samples of denoising results where MSE (Mean Signal Error) \& SNR (Signal to Noise Ratio) are the two standard parameters used to compare the performance of denoising. The assessment of comparative performance results for the denoising methods with that of the MWNFA algorithm suggest the affectivity of performance for the proposed method against the previous methods. The quality denoising without elimination of the features of the mammographic imagery data by MWNFA+RNN has improved the previous denoising technique and shall effectively make the medical practitioner to easily identify and consequently diagnose properly to the cancer influenced patients. ?? ?? (??, ?? ? ) = ? ?? ?? * ?? ð??"ð??" ?? , 
\section[{Conclusion}]{Conclusion}\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 1 :Step 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{234}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 2 :Figure 3 :Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5156}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Step 5 : 1 4GFigure 5 :Figure 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-6.png}
\caption{\label{fig_4}6GFigure 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{figure7}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-8.png}
\caption{\label{figure8}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-9.png}
\caption{\label{figure9}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.18766233766233764\textwidth}P{0.28149350649350646\textwidth}P{0.24837662337662336\textwidth}P{0.13246753246753246\textwidth}}
Cancer Influence\tabcellsep Size \& Bits\tabcellsep Resolutions\tabcellsep Scanners\\
Normal\tabcellsep 6.6GB 16 bits\tabcellsep 42 microns\tabcellsep DBA\\
Cancer\tabcellsep 6.6GB 16 bits\tabcellsep 43.5 microns\tabcellsep HOWTEK\\
Benign\tabcellsep 6.1GB 16 bits\tabcellsep 43.5 microns\tabcellsep LUMSIYS\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.23345070422535213\textwidth}P{0.14964788732394366\textwidth}P{0.15563380281690142\textwidth}P{0.14366197183098592\textwidth}P{0.16760563380281687\textwidth}}
Noise\tabcellsep Wiener\tabcellsep Wavelet\tabcellsep MWNFA\tabcellsep MWNFA+RNN\\
percentage\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
(\%)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
10.07\tabcellsep 3.53\tabcellsep 3.24\tabcellsep 5.68\tabcellsep 7.68\\
25.05\tabcellsep 15.97\tabcellsep 13.62\tabcellsep 17.89\tabcellsep 17.89\\
34.94\tabcellsep 22.43\tabcellsep 23.38\tabcellsep 26.34\tabcellsep 35.34\\
45.28\tabcellsep 33.33\tabcellsep 33.79\tabcellsep 37.12\tabcellsep 45.12\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 			\footnote{Modified Multi-Wavelet Noise Filtering Algorithm for Mammographic Image Denoising using Recurrent Neural Network} 			\footnote{© 2015 Global Journals Inc. (US)} 			\footnote{© 2015 Global Journals Inc. (US) 1} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[ DDSM: Digital Database for Screening Mammography]{b12}\label{b12} 	 		\textit{},  		 \url{http://marathon.csee.usf.edu/Mammography/Database.html}  	 	 		\textit{DDSM: Digital Database for Screening Mammography}  		 		 			University of South Florida Digital Mammography 		 	 
\bibitem[ Med. Imag ()]{b5}\label{b5} 	 		\textit{},  	 	 		\textit{Med. Imag}  		2000. 19  (2)  p. .  	 
\bibitem[Yu and Guan]{b4}\label{b4} 	 		\textit{A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films},  		 			S Yu 		,  		 			L Guan 		.  		IEEE Trans.  	 
\bibitem[Choubey et al. (2011)]{b11}\label{b11} 	 		‘A hybrid filtering technique in medical image denoising: Blending of neural network and fuzzy inference’.  		 			A Choubey 		,  		 			G R Sinha 		,  		 			S Choubey 		.  		 \xref{http://dx.doi.org/10.1109/ICEC-TECH.2011.5941584}{10.1109/ICEC-TECH.2011.5941584}.  	 	 		\textit{Electronics Computer Technology (ICECT), 2011 3rd International Conference on},  				8-10 April 2011. 1 p. 170.  	 
\bibitem[Mini and Thomas (2003)]{b3}\label{b3} 	 		‘A Neural Network Method for Mammogram Analysis Based on Statistical Features’.  		 			M G Mini 		,  		 			T Thomas 		.  	 	 		\textit{Proceedings of TENCON 2003},  				 (TENCON 2003)  		Oct. 2003. 4 p. .  	 
\bibitem[Ferreira and Borges ()]{b6}\label{b6} 	 		‘Analysis of mammogram classification using a wavelet transform decomposition’.  		 			Cbr Ferreira 		,  		 			D L Borges 		.  	 	 		\textit{Pattern Recognition Letters}  		2003. 24 p. .  	 
\bibitem[Pandey et al. ()]{b1}\label{b1} 	 		‘Fuzzy logic based microcalcification detection’.  		 			N Pandey 		,  		 			Z Salcic 		,  		 			J Sivaswamy 		.  	 	 		\textit{Proceedings of the IEEE for Signal Processing Workshop},  				 (the IEEE for Signal Processing Workshop)  		2000. p. .  	 
\bibitem[Sameti et al. (2009)]{b9}\label{b9} 	 		‘Image Feature Extraction in the Last Screening Mammograms Prior to Detection of Breast Cancer’.  		 			M Sameti 		,  		 			R K Ward 		,  		 			J Morgan-Parkes 		,  		 			B Palcic 		.  	 	 		\textit{IEEE Journal of selected topics in signal processing}  		Feb 2009. 3  (1)  p. .  	 
\bibitem[Heinlein et al. (2000)]{b0}\label{b0} 	 		‘Integrated Wavelet for enhancement of Microcalcification in Digital mammography’.  		 			P Heinlein 		,  		 			J Drexl 		,  		 			W Schneider 		.  	 	 		\textit{IEEE Transaction on medical imaging}  		Mar 2000. p. .  	 
\bibitem[Sentelle et al. (2002)]{b7}\label{b7} 	 		‘Multiresolution-Based Segmentation of Calcifications for the Early Detection of Breast Cancer. Real-Time Imaging 8’.  		 			S Sentelle 		,  		 			C Sentelle 		,  		 			M A Sutton ; R. Nakayama 		,  		 			Y Uchiyama 		,  		 			K Yamamoto 		,  		 			R Watanabe 		,  		 			K Namba 		.  	 	 		\textit{IEEE Transactions on Biomedical Engineering}  		2002. Feb 2006. 8  (2)  p. .  	 	 (Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms) 
\bibitem[Bao and Zhang (2003)]{b8}\label{b8} 	 		‘Noise reduction for magnetic resonance images via adaptive multiscale products thresholding’.  		 			Paul Bao 		,  		 			Lei Zhang 		.  	 	 		\textit{IEEE Transactions on Medical Imaging}  		Sep 2003. 22  (9)  p. .  	 
\bibitem[Metz (1989)]{b10}\label{b10} 	 		‘Some practical issues of experimental design and data analysis in radiological ROC studies’.  		 			C E Metz 		.  	 	 		\textit{Invest. Radiol}  		Mar. 1989. 24  (3)  p. .  	 
\bibitem[Strickland and Hahn ()]{b2}\label{b2} 	 		‘Wavelet transforms for detecting microcalcifications in mammograms’.  		 			R N Strickland 		,  		 			H L Hahn 		.  	 	 		\textit{IEEE Trans. Med. Imag}  		1996. 15  (2)  p. .  	 
\end{bibitemlist}
 			 		 	 
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