SKULL STRIPPING OF MRI USING CLUSTERING AND RESONANCE METHOD

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1 International Journal of Knowledge Management & e-learning Volume 3 Number 1 January-June 2011 pp SKULL STRIPPING OF MRI USING CLUSTERING AND RESONANCE METHOD K. Somasundaram 1 & R. Siva Shankar 2 1 Prof & Head, Dept. of Comp. Sci. and Applics, Gandhigram Rural Institute Deemed University, Gandhigram, Dindigul, Tamil Nadu, India, (ka.somasundaram@gmail.com) 2 Dept. of Comp. Sci. and Applics, Gandhigram Rural Institute Deemed University, Gandhigram, Dindigul, Tamil Nadu, India. (arjhunshankar@gmail.com) Abstract: Image segmentation is a significant and challenging factor in the medical image segmentation. This paper describes a novel skull stripping method for brain segmentation in MR Images. The conventional K-Mean Clustering technique has been used to find the brain boundary inside the skull. The intensity values are used to find the clusters. Then the centroids of the clusters are connected to form the brain boundary. Using the Resonance method for tracing the highest intensity values through all the clusters, skull area has been successfully removed and the clusters which are outside the skull region as well. This would facilitate to refine the edge of the brain by connecting the maximum intensity values. Keywords: Image Segmentation, Skull stripping, K-Mean Clustering, Centroids, Resonance method. 1. INTRODUCTION Magnetic Resonance Imaging (MRI) is a noninvasive, non-ionizing and non destructive method to study the structural anatomy of the human organs. MRI gives accurate results about the structure of soft tissues and organs. The MRI head scan images give the anatomy of brain that is supportive to diagnose the brain related diseases and deformities. The most significant advantage of the MRI is its ability to provide unprecedented contrasts between various organs and all soft tissues and the three-dimensional nature of imaging methods. MRI head scan images are used to do Image processing like Image Extraction, Image Enhancement, Image Registration, Image Compression, etc. Segmentation is an important process to extract information from complex medical images. The main objective of the image segmentation is to partition an image into mutually exclusive and exhausted regions such that each region of interest is spatially contiguous and the pixels within the region are homogeneous with respect to a predefined criterion. Widely used homogeneity criteria include values of intensity, texture, color, range etc. Brain segmentation can be achieved by skull stripping in MRI head scans. Skull Stripping methods are classified into three types. They are Intensity based, morphology based and deformable based. The extraction of the brain region from the non-brain region is done by methods like region growing, watershed algorithm, mathematical and morphological methods. Region based methods view brain regions as a group of connected pixel data sets. These regions will have muscles, cavities, skin, optic nerves, etc. Some previous work has used mathematical morphology like [1] - [8]. Hohne et al. [2] proposed a semiautomated segmentation algorithm based on region growing and morphological operations. Justice et al. [4] proposed a semi automated segmentation method using 3D seeded region growing (SRG) which is an extension of 2D SRG method proposed by Adams et al. [7]. One disadvantage of these methods is that the user has to select the seed region and the threshold values. Intensity based methods work on modeling the intensity distribution used for threshold classification. The basic principles used in skull stripping are detailed in section II. The principle of the proposed technique is described in section III. The results and discussions are given in section IV and the conclusion in section V.

2 20 International Journal of Knowledge Management & e-learning 2. BASIC PRINCIPLES USED IN SKULL STRIPPING In this section the underlying principle of skull stripping is described Preliminary Step Initial seed point must be located at the mid point of the image where the coordinate values are divided by two. After determined seed pixel, the conventional K-Mean Clustering technique is used for clustering Clustering A large number of clustering definitions can be found in the literature, from simple to elaborate. The simplest definition is the grouping together of similar data items into clusters. A simple, formal, mathematical definition of clustering let X R m n a set of data items representing a set of m points x i in R n. The goal is to partition X into K groups C k such every data that belong to the same group are more alike than data in different groups. Each of the K groups is called a cluster. K-Mean Clustering Algorithm K-means one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centroids, one for each cluster. These centroids should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest centroid. When no point is pending, the first step is completed and an early group age is done. At this point we need to re-calculate k new centroids as barycentre of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new centroid. A loop has been generated. As a result of this loop we may notice that the k centroids change their location step by step until no more changes are done. In other words centroids do not move any more. The steps involved in KM algorithm are given below. 1. Set the cluster size as K. 2. Initialize the centroid of each cluster Ci=0, i=1,,,,k. 3. Process the observations and assign to a cluster. 4. Find the centroids of each cluster. The centroid of a cluster is found by minimizing the objective function 2 J = Σ Σ P C 1 () j k n i j i J = 1 (1) where P i (j) C j 2 the distance between the centroid, Pi is the j th pixel of the ith cluster, Cj is the j th pixel of the centroid Fixing the Centroids The Maximum intensity pixels will have a centroid pixel which may be a maximum intensity among the each cluster regions. They commonly defined by Centroid_X = X(i)/2; (2) Centroid_Y = Y(i)/2; Where X,Y where axis points and i will be the count of pixels of cluster regions. When even numbers arise at summing up in cluster region, maximum values among them will be produced as centroid. 3. THE PROPOSED METHOD 3.1. Significance of Resonance & Implementation In this paper we propose a method based on the vibration of a string. The frequency of vibration depends on the density of the string. Here we use the intensity of the pixel as the density. The Frequency of the vibration of a string is given by F = C K/P (3) where F is the Resonance Value, C and K are Constant value and P is the Unit Mass Density of the string. A small variation of an input (P) value can produce great impact in the resultant value. Resonance method is facilitating along with the image intensity values of the MRI image to find the variations. In this paper P value is assumed to be current pixel intensity values. From a seed point, we determined by k means algorithm, we move around the pixels in MRI. We start from the seed

3 Skull Stripping of MRI Using Clustering and Resonance Method 21 point and propagate along the points, when maximum frequency captured using eqn (3) is obtained. For this we move scan about 4 pixels in all directions and determine the frequency. We move to next point which gives the highest frequency until we get the same point again. By this process we are able to detect the boundary. The co-ordinate values of the high level variation sets added to the largest connected component. The connected component will give the edge The Process Chart 3.2. Stripping Skull Region By the clustering technique some times the areas outside the skull may have pixels related to the clustering of maximum intensity values. We can easily avoid them by our approach itself. We start from the centre of the brain region to get the seed point which is a centroid of a cluster group. While growing the adjacent pixels by the values, we can move through the edges only, which are found inside the skull region. Finally skull area won t get into this process of Resonance. Approx Edge will be between maximum intensity values and mean values of the clusters. The Areas inside the Brain edges will be brain only, so the hole filling operations are not done here Projecting the Brain Region Skull stripping and brain projection are the last process, which are co-related with each one. We use already stored values of largest connected component in a resultant array. We project the pixels in between the values of the row wise projecting method from left to right end of the largest connected values Process Through Slices We start this process from the Middle slice and then proceed through all the slices in the volume existing first and last slice. We apply the Resonance method and get a brain portion in the middle slice, it will be the largest & finest area covered as the brain portion in that volume. We can store the coordinate values of the edge in the middle slice and we can process the inner portion further to the rest of the slices. After finding the edge of the next slices that will be marked coordinate values to be processed for the next slice until we reach the first and last slice of the volume. This makes our process robust persistence and reliable. The time complexity will be reduced while calculating the entire volume. Figure 1: Flow Chart of the Proposed Method 4. RESULTS AND DISCUSSIONS The performance of the proposed method is tested using MRI Axial T2 weighted volume having 56 slices from the Whole Brain Atlas (WBA). The results are recorded in terms of visual perception, Jaccard and Dice values. It is clear from the visual perception of the proposed method that extracts the brain region from the skull. The performance of the proposed method is illustrated using the original images and extracted brain images are shown as figure sets Figure 2 and Figure 3.

4 22 International Journal of Knowledge Management & e-learning To further confirm the quality of the proposed method, Jaccard Similarity and Dice similarity are used and the value obtained between 0 to 1. The best results will be very close to 1. The chosen values are from both agreement and both disagreement Measures. Consider that A, B are the set of the pixel values in our segmented Image and hand segmented Image given by IBSR. Jaccard Similarity: J( A,) B Dice Similarity: D( A,) B = = A + B (4) (5) The recorded average values of Jaccard and Dice for the volume by the proposed method are furnished in Table 1. Data Set Table 1 Proposed Method Type Jaccard Dice Axial T2 Normal CONCLUSION In this Paper we have proposed a novel method to extract brain portion from MRI head scan images. The proposed method is based on clustering and Resonance method. Our method is able to detect the boundary to separate brain portion and skull directly and thus avoids the processing of background and skull areas. Figure 2: Raw Images Figure 3: Extracted Images REFERENCES [1] Brummer, M. E., Mersereau, R. M., Eisner, R. L., Lewine, R. R. J., Automatic Detection of Brain Contours in MRI Data Sets. IEEE Trans. Med. Imag. 12 (2), 1993, [2] Hohne, K. H., W. A., Interactive 3D Segmentation of MRI and CT Volumes Using Morphological Operations, J. of Comput.Assist. Tomogr., 16 (2), 1992, [3] John, C., Kevin, W., Emma, L., Chao, C., Barbara, P. Declan, J. Statistical Morphological Skull Stripping of Adult and Infant MRI Data, Comput. Biol. Med., 37 (3), [4] Justice, R. K., Stokely, E. M., Strobel, J. S., Ideker, R. E., Smith, W. M., Medical image segmentation using 3D seeded region growing. Proc. SPIE Med. Imag. 3034, 1997,

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