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\begin{document}

             \author[1]{Ayush  dogra}

             \affil[1]{  punjabi university}

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\date{\small \em Received: 8 December 2012 Accepted: 1 January 2013 Published: 15 January 2013}

\maketitle


\begin{abstract}
        


Multimodal image matching calls or demands for feature based or object based matching. Feature used in matching can be edges ,ridges, blobs, valleys etc. Feature based matching is considered as less tedious and low level image processing task whereas object based matching is consider as high level image processing task and complex.

\end{abstract}


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\let\tabcellsep& 	 	 		 
\section[{Feature based Matching of CT \& MRI Brain Images Ayush Dogra}]{Feature based Matching of CT \& MRI Brain Images Ayush Dogra}\par
Abstract -Multimodal image matching calls or demands for feature based or object based matching. Feature used in matching can be edges ,ridges, blobs, valleys etc. Feature based matching is considered as less tedious and low level image processing task whereas object based matching is consider as high level image processing task and complex.\par
I. 
\section[{Matching}]{Matching}\par
hile integrating two multimodal and mono modal images, first step is matching (geometrical matching) and second step is fusion (combined display of data involved). In this paper we will concentrate on first step. Matching of CT and MRI images is useful in radiation therapy planning. This type matching is also used in skull base surgery and epilepsy surgery.  And the generalization of a 3D images by lvv operator can be found in \hyperref[b3]{[4]}. When applying lvv operator to the CT set the resultant image show skull ridge. Then this ridgeness image is superimposed on to the original    
\section[{Registration Method}]{Registration Method}\par
Now we need to register the feature volume. The technique used is cross correlation \hyperref[b4]{[5]} since the two images have different physical realities. By using the grey values directly \hyperref[b0]{[1]}, avoid segmentation our feature images.\par
The correlation value c(t) of CT feature volume L1 and MR feature volume L2 over all rigid transformations t, where c(t) is defined Here next level is formed by maximizing the trough images or minimizing the ridge images. This method is also called multi resolution correlation \hyperref[b1]{[2,}\hyperref[b5]{6]}. The disadvantage of this approach is high computational effort required. "   
\section[{Conclusion}]{Conclusion}\par
The terms matching and registration are both used to denote the process of determining a transformation that relates the contents of two images in a meaningful way. The registration of MR and CT images was one of the first application of medical image registration. Further more the applicability of MR-CT registration is restricted primarily to the head. MR-CT registration is likely to become more widely used in future.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{fig_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11}\includegraphics[]{image-8.png}
\caption{\label{fig_6}Figure 11 :}\end{figure}
 			\footnote{© 2013 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Van Den Elsen et al.]{b5}\label{b5} 	 		‘Automatic registration of CT and MR brain images using correlation of geometrical features’.  		 			P A Van Den Elsen 		,  		 			J B A Maintz 		,  		 			E J D Pol 		,  		 			M A Viergever 		.  	 	 		\textit{IEEE Transactions on medical images}  		 	 
\bibitem[Van Den Elsen et al. (1995)]{b4}\label{b4} 	 		‘Automatic Registration of CT and MR Brain Images Using Correlation of Geometrical Features’.  		 			P Van Den Elsen 		,  		 			J Maintz 		,  		 			E-J Pol 		,  		 			M Viergever 		.  	 	 		\textit{IEEE Trans. Medical Imaging}  		June 1995. 14  (2)  p. .  	 
\bibitem[Maintz et al. ()]{b3}\label{b3} 	 		‘Evaluation of ridge seeking operators for multimodality medical image matching’.  		 			J B A Maintz 		,  		 			P A Van Den Elsen 		,  		 			M A Viergever 		.  	 	 		\textit{IEEE Transactions on pattern analysis and machine intelligence}  		1996. 18  (4)  p. .  	 
\bibitem[Van Den Elsen et al. ()]{b0}\label{b0} 	 		‘Grey value correlation techniques used for automatic matching of CT and MR brain and spine images’.  		 			P A Van Den Elsen 		,  		 			E J D Pol 		,  		 			T S Sumanaweera 		,  		 			P F Hemler 		,  		 			S Napel 		,  		 			J Adler 		.  	 	 		\textit{Visualization in Biomedical Computing, Proc. SPIE},  				 (Bellingham, WA)  		1994. SPIE Press.  	 	 (in press) 
\bibitem[Van Den Elsen et al. ()]{b2}\label{b2} 	 		‘Image fusion using geometrical features’.  		 			P A Van Den Elsen 		,  		 			J B A Maintz 		,  		 			E J D Pol 		,  		 			M A Viergever 		.  	 	 		\textit{Visualization in biomedical computing, volume 1808 of Proc. SPIE},  				 			R A Robb 		 (ed.)  		 (Bellingham, WA)  		1992. SPIE Press. p. .  	 
\bibitem[Van Den Elsen ()]{b1}\label{b1} 	 		\textit{Multimodality matching of brain images},  		 			P A Van Den Elsen 		.  		1993. Netherlands.  		 			Utrecht University 		 	 	 (PhD thesis) 
\end{bibitemlist}
 			 		 	 
\end{document}