Brain Portion Peeling from T2 Axial MRI Head Scans using Clustering and Morphological Operation

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1 159 Brain Portion Peeling from T2 Axial MRI Head Scans using Clustering and Morphological Operation K. Somasundaram Image Processing Lab Dept. of Computer Science and Applications Gandhigram Rural Institute (Deemed University) Gandhigram, Dindigul. Tamil Nadu, India Abstract Automatic brain segmentation methods are useful but are computationally intensive tools in medical image processing. In this study, we propose a brain segmentation method based on clustering and morphological operation. Initially, from the middle slice of the whole volume, the brain portion is segmented and using this as a reference, brain portion in the remaining slices are extracted. Experiments using the proposed method on two T2 weighted data set show that the proposed method gives satisfactory results. Keywords- MRI head scans, clustering, morphological operation I. INTRODUCTION Magnetic Resonance Images (MRI) is one of the popular imaging technique which is based on Nuclear Magnetic Resonance (NMR). MRI technique is one of the leading technologies used in the medical imaging. MRI is a nonionizing, non-destructive and non-invasive method. MRI aids the clinical persons to diagnose the disease, surgery planning, and to plan for the treatment. The MRI scans are taken in three different types such as T1-Weighted, T2-Weighted and Proton Density (PD). Each type differs in their relaxation time. The MRI scans are taken in three orientations, axial (top to bottom of the body), coronal (rear to front of the body) and sagittal (right to left of the body) [1]. MRI is used to take images of human head. Major part of the head portion is brain and it is divided into three parts cerebrum, cerebellum and brain stem. The brain is covered by Cerebral Spinal Fluid (CSF) and skull prevents certain physical deformation of spongy bone and from external mechanical shocks [2]. The brain may be affected by degenerative disease, bleeding, tumors, infections and hormonal disorders. These diseases can be identified in various ways by means of imaging and one of the important and accurate medical imaging methods is MRI. The brain structures complicated, and it is hard to identify the abnormality in the brain. Clinical persons need some clear perception of brain and MR imaging of human head is useful for that. Hand stripping is a difficult and time consuming and operator biased and hence some automated method for segmenting the brain portion is needed. A wide variety of algorithms have been developed for the segmentation of brain portion from MRI of head scans since K. Ezhilarasan Image Processing Lab Dept. of Computer Science and Applications Gandhigram Rural Institute (Deemed University) Gandhigram, Dindigul. Tamil Nadu, India ezhilarasankc@gmail.com Some of them are, Kapur et al., proposed a skull stripping method [3], Lemieux [4], brain extraction tool (BET)[5], Brain Surface Extractor (BSE)[6] and Hybrid watershed algorithm (HWA)[7]. Some contemporary methods are available for good segmentation process, threshold values, labeling and run length encoding used in Brain Extraction Algorithm (BEA) for T1 images [8]-[10]. In this article we propose a simple automatic segmentation method to segment the brain portion from T2-axial MR Images. We make use of edge detection, binary morphological operation and largest connected component analysis to extract the brain portion. The data sets for our experiments are taken from the Whole Brain Atlas (WBA)[11]. The results of the proposed method indicate that this segmentation method is capable of yielding more accurate results than that of the popular method (BET). The remaining part of the paper is organized as follows. Section 2 presents the proposed method and the materials used. In section 3, results and discussion are given. Section 4 concludes the paper. II. PROPOSED METHOD The proposed method has two stages. In stage 1, the brain is extracted from the middle slice of the given volume. In the second stage, the extracted brain from the previous adjacent slices is considered as a reference and brain portion is extracted for the remaining slices. The stage1 flow chart is shown in Fig. 1 and the overall flow chart of the proposed method is shown in Fig.2. In stage 1, binarization, clustering, morphological operation and largest connected component (LCC) analysis are accomplished to generate the brain mask. Using this, the brain portion is extracted. A. Binary Image Creation Two separate processes A, B are run in parallel. Process A is binarization, it generates image I B and process B is clustering which results in image I C. The binary image is generated from the input image I using threshold value T. Threshold value T is calculated using Riddler s method. This method is an iterative method and it generates an optimal solution. The initial threshold value T 1 is computed as:

2 160 where, m x n is the image size N is the total number of pixels in I(x, y). The value T 1 is used to separate the pixel values into two sets G 1 and G 2. where f is the image I C. Each element in the image has coordinates which is denoted by f(x,y). G x is the change of intensity value in x direction and is given by: and the change value in x direction is calculated as: Then the threshold value T is computed as: Similarly, the change in y direction is denoted as, where, c 1 and c 2 are the count of pixels in sets G 1 and G 2. Equations (2) and (3) are iteratively computed until the value of T and T 1 comes closer. The optimal threshold value of T 1 is T. The binary image I B is obtained as: and the change of -y direction is calculated as : The image I E is obtained by using the following inequality condition: B. Intensity Clustering The other process B starts from clustering. The MRI of head scan contains brain portion with gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), ear, nose, meninges, etc., each of them differ in their gray levels. Depending on the shades of gray, the image is separated into five different clusters namely, R1, R2, R3, R4 and R5 [12]. The formation of cluster is given below. The clustering is done using the intensity of the pixels as: Where, T is the threshold value which is obtained by (3). Now the image I E contains the boundaries of the head portions. D. Removal of edges Removing connection between the brain and the non brain tissues will produce an image with edges removed. Hence, by subtracting I E from I B, we obtain the binary image I S as: Sta A Generate Binary Image I S = I B- Input I B Intensity Clustering Edge Erosion I Er where, the integers 0-255, represent the gray levels of the input image I(x, y). Any pixel in f(x, y) which has intensity value less than 50 is set to 0 and it is treated as background. The intensity values which falls under the cluster R2 takes the maximum range of that cluster. Using this clustering process the image I C is obtained. C. Edge Detection From the image I C the edges are obtained by using the first order derivative of the intensity value. The first order derivatives are calculated for all the four direction G x, G -x, G y, G -y. These derivatives are calculated as follows: LCC I LCC Dilation I M Output Brain Sto Fig. 1 Flow Chart of Stage 1 of the proposed method.

3 161 Start Read a Volume N = number of slices M =N/ 2, i=1, fl = -1, I= f(m) N (M-i)> -1 Stage 1 Brain Mask I M fl < 0 Y (M+i)<N E. Erosion The image I S, still has some non brain regions which are connected with the brain tissues. Such pixels are removed by morphological erosion operations. The erosion operation applied on image I S as: where, B is the structuring element of size 3x3. We experimented with structuring elements with various sizes, 3x3, 5x5 and 7x7, but the size 3x3 gave the best result for the axial images. The structuring element is allowed to move over the image I s and eroded image I Er is obtained. F. Largest Connected Component (LCC) Analysis The image I Er contains several isolated regions which belong to both brain and non brain regions. In middle brain region is the largest connected (LCC) component among these isolated regions. LCC obtained using on run length scheme for region labeling and selection [10]. The image I LCC is obtained by applying LCC procedure. I = Read f(m-i) I = Read f(m+i) I D Overlap I and I M I D Overlap I and I M I Eros = Erosion (I D) I Eros = Erosion (I D) The image I LCC contains the largest connected area which is the brain portion. G. Dilation Some of the outer layer of the brain was eroded during erosion operation. To recover them back, dilation is performed on the image I LCC. The dilated image I M is obtained as: I Dila = Dilation (I Eros ) I Dila = Dilation (I Eros) Save (I Dila ), i++, I M = I Dila Save (I Dila ), i++, I M = I Dila M+i = = N N where, B is the structuring element. We processed with different shapes of structuring element such as disk, square and diamond and found that disk shaped structuring element gave the best result. The size of the structuring element 5x5 is selected after various tests conducted between 5x5, 7x7 and 11x11. The disk shaped structuring element of size of 5x5 is shown in Fig. 3. Y fl =1,i=1,I M=f(M). Save( I M) Output: Save all images Stop Fig. 2. Overall flow chart of our proposed method. Fig. 3 Structuring Element B Now the I M is the brain mask. By using the brain mask the brain portion is extracted. Taking the middle slice as a divider, the total slices in the MRI volume are separated into two groups, upper slices (US) which are lying above the middle slice and lower slices (LS) which are lying below the middle slice. We start with the US one by one, after completing the US, we start LS to extract the

4 162 brain. In both the cases, the brain extracted in the previous slice is taken as the mask for the current slice. Every step in the process depends on previous operation. Any error during any level of the process will affect the remaining process. III. RESULTS AND DISCUSSIONS We carried out experiments by applying proposed algorithm on two volumes of axial head scans of MRI taken from the WBA. The brain portion extracted is shown in Fig. 3. For visual comparison, the results for few slices obtained by BET and proposed method are given in Fig. 4. BET does not work well for those slices which have temporal lobe (slices number 13 to 16). However, our method worked well. Original Image Propose Method BET Result Fig. 4 Brain extracted by the proposed method and BET. Row1 shows the original image. Row2 shows the brain extracted by our method and Row3 by BET IV. CONCLUSION In this article, we have proposed an automatic brain extraction method for MRI of head scans. Experimental results show that the proposed method performs better than the standard method BET. ACKNOWLEDGEMENT This work is funded by the University Grants Commission, New Delhi, through the Grant No: F No 37/154/2009(SR). Whole Brain Atlas (WBA) provided two volumes of T2-MRI. Aaaaa (a) (b) Fig. 3 Brain extracted using the proposed method. (a) Original image. (b) Brain extracted from (a). Slice No REFERENCES [1] A.P. Dhawan, Medical Image Analysis, 2 nd Edition, John Wiley & Sons, Inc., Hoboken, New Jersey, ISBN , [2] T.Kalaiselvi, Brain Portion Extraction and Brain Abnormality Detection from Magnetic Resonance Imaging of Human Head Scans,. Pallavi publications India Pvt. Ltd., Erode, ISBN , [3] T.Kapur, Grimson WEL, III WMW, R.Kikinis, Segmentation of brain tissue from magnetic resonance images, Medical Image Analysis, vol. 12, pp , [4] L.Lemieux, G.Hagemann, K.Krakow, F.G. Woermann, Fast, accurate, and reproducible automatic segmentation of the brain in T1-weighted volume MRI data, Magnetic Resonance Med. vol. 42 (1), pp , [5] S.Smith, Fast robust automated brain extraction, Human brain mapping vol. 17 (3), pp , [6] S.Sandor, R.Leahy, Surface-based labeling of cortical anatomy using a deformable atlas, IEEE Tans on Medical Imaging,vol. 16, pp , [7] S egonne F, Dale AM, Busa BE, Glessner BM, Salat BD, Hahn BHK, A BF, A hybrid approach to the skull stripping problem in MRI. NeuroImage, vol. 22, pp , [8] K.Somasundaram, T.Kalaiselvi, Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations, Computers in Biology and Medicine, vol. 41, pp , [9] J.Francisco, D.Galdamesa, D.Fabrice Jailletc, A.Claudio, B. Pereza, An accurateskullstripping method based on simplexmeshes and histogram analysis for magnetic resonance images, vol. 206 (2), pp , [10] K.Somasundaram, T.Kalaiselvi, Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance

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