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\title{Tumor Extraction and Volume Estimation for T1-Weighted Magnetic Resonance Brain Images}
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\begin{document}

             \author[1]{  S.Satheesh}

             \author[2]{Dr.K.V.S.V.R  Prasad}

             \author[3]{Dr.K.V.S.V.R  Prasad}

             \affil[1]{  G. Narayanamma Institute of Technology and Science, Hyderabad.}

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\date{\small \em Received: 13 December 2011 Accepted: 5 January 2012 Published: 15 January 2012}

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\begin{abstract}
        


Magnetic  resonance  imaging (MRI)  is  a  significant  imaging  technology  for brain tumor diagnosis because   physicians can identify precise pathologies by studying   the  variations  of  tissue  characteristics  that  occurs  in    various kinds of MR images. Segmentation of MRI is a   pre-process in determining the volume of different brain   tissues, but here tumor detection is of primary concern.   We proposed a method to extract tumors as seen through   MR brain images using co-clustering and morphological   operations  and  its  volume  estimation  was  done  by   Cavalier?s  estimator  of  morphometric  volume  method.   Quantitative analysis showed that the proposed method   yielded better results in  comparison with fuzzy c-means   algorithm (FCM)

\end{abstract}


\keywords{tumor extraction, co -clustering, morphological operations, skull removal, magnetic resonance imaging, volume estimation.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
umor is one of the most common brain diseases; hence, its diagnosis and treatment are compulsory. In recent years, developments in medical imaging techniques have aided experts in numerous domains of medicine, such as computeraided diagnosis, follow-up of pathologies, surgical planning and surgical guidance. Among the available medical imaging techniques, MRI is a popular neuroimaging technique for the evaluation and treatment of brain tumors. MRI devices generate images in sagittal, axial, and coronal planes that give a better localization of a lesion in a 3D space of the brain.\par
The segmentation of MR brain images is a complex task, as it involves a large amount of data. Few artifacts might arise due to a patient's motion or limited acquisition time; also, soft-tissue boundaries of tumor are vaguely defined. There exist a numerous classes of tumors with different shapes and sizes that appear at any location with dissimilar image intensities. A few of them might also deform the nearby structures or be associated to necrosis or edema that affects the image intensities in and around the tumor.\par
A number of popular brain tumor segmentation methods are briefly mentioned here. Clark et al. \hyperref[b0]{[1]} proposed a tumor segmentation method by a set of rules with fuzzy classification and a knowledge base. Dou et al. \hyperref[b2]{[3]} proposed a fuzzy information fusion frame work for tumor segmentation. Gordillo et al. \hyperref[b3]{[4]} developed a fully automatic and unsupervised brain tumor segmentation method. Hassan Khotanlou et al. \hyperref[b4]{[5,}\hyperref[b5]{6]} introduced a tumor segmentation using symmetry analysis, fuzzy classification, and spatially constrained deformable models. Jingxin Nie et al. \hyperref[b8]{[9]} proposed expectation maximization and a spatial accuracyweighted hidden markov random field for tumor segmentation. Vida Harati et al.  {\ref [15]} proposed an automatic tumor segmentation technique based on improved fuzzy connectedness algorithm.\par
Despite various efforts and promising results in the medical imaging area, exact segmentation and characterization of tumors are still difficult and challenging.\par
This paper proposes a method that combines coclustering and morphological operations such as erosion, dilation and hole filling \hyperref[b13]{[12]}. Firstly, morphological operations \hyperref[b14]{[13,}\hyperref[b15]{14]} are performed on contrast-enhanced axial T1-weighted MR Head scans to remove non-brain data (skull, fat, skin, and muscle) to enhance tumor segmentation efficiency. These external tissues often interfere with a brain tissue during segmentation which accounts for poor segmentation efficiency. Secondly, coclustering algorithm \hyperref[b1]{[2,}\hyperref[b10]{10]} is applied to segment brain tumor, and the volume of the tumor was evaluated by taking patient 1 of Table \hyperref[tab_0]{I}. Performance of the proposed method was judged by comparing it with the most popular FCM algorithm \hyperref[b7]{[8]}. 
\section[{II.}]{II.} 
\section[{Methodology}]{Methodology}\par
Our proposed method was divided into two parts. The output obtained from one part was taken as an input to the next part. The process flow chart is given in Figure1.The skull region was removed in stage I by morphological operations, and the tumor was extracted in stage II using co-clustering algorithm. Abstract -Magnetic Resonance Imaging (MRI) is a significant imaging technology for brain tumor diagnosis because physicians can identify precise pathologies by studying the variations of tissue characteristics that occurs in various kinds of MR brain images. Segmentation of MRI is a pre-process in determining the volume of different brain tissues, but here tumor detection is of primary concern. We proposed a method to extract tumors as seen through MR brain images using coclustering and morphological operations and its volume estimation was done by Cavalier's estimator of morphometric volume method. Quantitative analysis showed that the proposed method yielded better results in comparison with Fuzzy C-Means algorithm (FCM).\par
The conventional analysis of MR brain images with tumor by an expert is a complex and timely process. Thus, an automatic tumor segmentation method is desirable to give an adequate performance. As a result, tumor volume can be evaluated for follow-up of the disease effectively. The objective of the process was to generate the brain region of the axial T1-weighted MR head scans by morphological operations namely, erosion and dilation \hyperref[b14]{[13,}\hyperref[b15]{14]}.\par
Erosion operation was performed on the selected image (I) and used to split the image's weakly attached regions. The image was eroded by an octagonal structuring element. The eroded image I 1 , would have several disconnected regions and was obtained using (1).\par
()\textbf{1}\par
Where B is the octagonal structuring element with a size of The preceding process decomposed the given image into several isolated regions. Then, the eroded image was converted to a binary image, I 2 , using (2).\par
(2) recapture the brain tissues that were lost in the process of erosion. It was done with the same structuring element that was used for erosion. The dilated image, I 3 , was obtained using (3).\par
(3) Thereafter, the binary mask was obtained by filling the holes in the dilated image. The binary mask was, thus, convolved with the original image to get the final brain portion. The results of the skull removal process are shown in Figure  {\ref 2}.  \par
b) Volume estimation Cavalier's estimator of morphometric volume method \hyperref[b12]{[11]}.The Cavalier's method was formulated on equally spaced slices of images using equation ( 11).\par
Where'd' is the distance between each slice, 'y i ' is the area of slice 'i', 'n' is the total number of slices, 't' is the slice thickness, and 'y max ' is the maximum value of 'y'.\par
IV. 
\section[{Results and discussions}]{Results and discussions}\par
The proposed method was verified on MRI brain image data sets of five patients named as patient 1 to patient 5, and one slice was selected from each patient's data set in random to evaluate the performance of the proposed method. Volume estimation has been done by taking all necessary slices of patient 1. These data sets have been acquired on a Philips Achieva 1.5T apparatus by an axial T1-weighted sequence. The slice thickness for patient 1 was 1.5mm, and the spacing between the adjacent slices was 0.9mm. The details of the data sets used are given in Table \hyperref[tab_0]{I}. Data sets were collected from the Department of Radiology and Imaging Science, Apollo Health City, Hyderabad, India.\par
The evaluated segmentation results at pixel level are shown in Table \hyperref[tab_1]{II}, and the extracted result of a meningeal tumor is shown in Figure  {\ref .}4 for the slice 105 of patient 3. It can be observed that the results exhibit close proximity to the manually segmented images by the experts and are superior to FCM. Quantitative results obtained by the proposed method in comparison with FCM are given in Table \hyperref[tab_2]{III}.\par
From the Table \hyperref[tab_2]{III}, the SI of the proposed method varies from 87.73\% to 94.08\% but for FCM it is The evaluation of tumor extraction results by the proposed method and the FCM algorithm was compared with the manually segmented tumors to measure its effectiveness. The manual segmentations were provided by medical experts, which might include abnormal tissues, like edema, along the tumor region. Let M be the manually segmented tumor and A be the segmented tumor by the proposed method or the FCM algorithm asshown in Figure  {\ref 3}. Here, the Similarity Index (SI), Correct Detection Ratio (CDR), Under Segmentation Error (USE), Over Segmentation Error (OSE), Hausdorff Distance (HD) and Average Surface Distance (ASD) were used for efficiency evaluation.\par
SI is a measurement that gives the true segmented region, which is relative to the total segmented region in both the segmentations.CDR value indicates the degree of trueness of the actual tumor. USE is the ratio of the number of voxels falsely identified as tumor portion by the proposed method to the manually segmented tumor. OSE is the ratio of number of voxels falsely identified non tumor region by the proposed method to the manual segmented tumor. Total Segmentation Error (TSE) is the sum of USE and OSE. HD is the largest difference between two surfaces and ASD illustrates how much the two surfaces differ on average. All these performance metrics also apply to FCM algorithm. The evaluation metrics SI, CDR, USE, OSE, HD, and ASD were obtained by using equations (  {\ref 5}), ( 6), (  {\ref 7}), (  {\ref 8}), (  {\ref 9}) and (  {\ref 10}) respectively \hyperref[b4]{[5,}\hyperref[b5]{6,}\hyperref[b6]{7,} {\ref 15]}.\par
Where True Positive (TP) is the number of pixels detected correctly, False Positive (FP) is the number of pixels detected falsely as tumor and False Negative (FN) is the number of pixels detected falsely as non tumor. 
\section[{HD (M,A) = max (h(M,A), h (A,M))}]{HD (M,A) = max (h(M,A), h (A,M))}\par
Where ?(??, ??) = ?????? ?? ??? ?????? ????? ??(??, ??) , and ??(??, ??)denotes the Euclidean distance between ?? and ?? (?? and ?? are points of M and A respectively)\par
The volume of tumor V t was estimated by 72.16\% to 87.76\% .The CDR of the proposed method ranges from 85.52\% to 95.86\%, and for FCM it is 85.69\% to 91.61\%. The TSE of the proposed method changes from 12.07\% to 26.24\%, and for FCM it is 24.75\% to 70.69\%.HD ranges from 5.4772 to 6.8557 for the proposed method and 5.6569 to 6.8557 for FCM, which proves a good position of the periphery of the tumors of the proposed method. ASD changes from 0.6375 to 1.3081 for the proposed method and 0.8761 to 1.5530 for FCM. The volume of tumor for patient 1 is shows close proximity as shown in Table \hyperref[tab_3]{IV}.  V. 
\section[{Conclusion}]{Conclusion}\par
The presented tumor segmentation method was tested on five abnormal MRI brain slices of different patients, and the volume was evaluated for one of the patients. It was observed that integrating co-clustering with morphological operators minimizes segmentation error when compared with FCM. The qualitative evaluation of the obtained results for the proposed tumor extraction method achieved a good performance. A future scope in this area intends in finding the type of tumor based on ontology of tumors and segmentation of edema.  \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{273}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Figure 2 : 7 .Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_6}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-7.png}
\caption{\label{fig_7}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{I} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_0}Table I :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{II} \par 
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\caption{\label{tab_1}Table II :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{III} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_2}Table III :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{IV} \par 
\begin{longtable}{P{0.0681890389197776\textwidth}P{0.13772835583796664\textwidth}P{0.01822875297855441\textwidth}P{0.4185861795075457\textwidth}P{0.024305003971405878\textwidth}P{0.016203335980937254\textwidth}P{0.017553613979348688\textwidth}P{0.025655281969817315\textwidth}P{0.0715647339158062\textwidth}P{0.008776806989674344\textwidth}P{0.043208895949166\textwidth}}
\tabcellsep \multicolumn{7}{l}{Tumor Extraction and Volume Estimation for T1-Weighted Magnetic Resonance Brain Images}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Patient\tabcellsep Gender\tabcellsep Age\tabcellsep Tumor Type\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 1\tabcellsep Male\tabcellsep 54\tabcellsep Meningioma\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 2\tabcellsep Male\tabcellsep 42\tabcellsep Cystic glioma\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 3\tabcellsep Female\tabcellsep 48\tabcellsep Meningioma\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 4\tabcellsep Male\tabcellsep 31\tabcellsep Dural tumor\\
2012\tabcellsep \multicolumn{6}{l}{41,486mm 3 for the proposed method when compared with the value of 43,547mm 3 for manual segmentation}\tabcellsep 5\tabcellsep Male\tabcellsep 46\tabcellsep Metastatis\\
Year\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
12\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Volume XII Issue XII Version I\tabcellsep \tabcellsep \tabcellsep Patient 1 2 3 4 5\tabcellsep Slice Number 83 95 105 112 120\tabcellsep TP 420 988 1255 1201 797\tabcellsep FCM FP 243 133 191 258 542\tabcellsep FN 64 165 159 150 73\tabcellsep Proposed Method TP FP FN 454 97 30 986 92 167 1239 12 175 1260 78 91 834 69 36\\
D D D D ) D\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
(\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Computer Science and Technology\tabcellsep Patient 1 2 3 4 5\tabcellsep Slice No. 83 95 105 112 120\tabcellsep \multicolumn{5}{l}{FCM OSE 72.23 86.78 50.21 13.22 63.43 5.6569 0.8761 87.73 93.80 20.04 Proposed Method SI CDR USE TSE HD ASD SI CDR USE OSE TSE 6.20 26.24 5.4772 0.6375 HD ASD 86.90 85.69 11.54 14.31 25.85 6.3246 1.1738 88.39 85.52 7.98 14.48 22.46 6.4807 1.1296 87.76 88.76 13.51 11.24 24.75 6.7823 1.4961 92.98 87.62 0.85 12.38 13.23 6.5574 1.3025 85.48 88.90 19.10 11.10 30.20 6.8557 1.5530 93.72 93.26 5.77 6.74 12.51 6.8557 1.3081 72.16 91.61 62.30 8.39 70.69 6.2450 1.4178 94.08 95.86 7.93 4.14 12.07 6.4031 0.9403 Volume of tumor using manual Volume of tumor using the segmentation (mm 3 ) proposed method (mm 3 )}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep 43,547\tabcellsep \tabcellsep \tabcellsep 41,486\end{longtable} \par
 
\caption{\label{tab_3}Table IV :}\end{figure}
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\end{document}
