LOCATING BRAIN TUMOUR AND EXTRACTING THE FEATURES FROM MRI IMAGES

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1 Research Article OPEN ACCESS at journalijcir.com LOCATING BRAIN TUMOUR AND EXTRACTING THE FEATURES FROM MRI IMAGES Abhishek Saxena and Suchetha.M Abstract The seriousness of brain tumour is very high among all types of cancers. So immediate detection and proper treatment can save a person s life. In this paper a system for brain tumour extraction is designed. In the pre-processing stage, noise is removed and the texture features are extracted from it. Then with the help of the obtained features classification of images into normal and abnormal is done using morphological classifier. Then on abnormal image bounding box algorithm is used to locate the tumorous part. After this morphological is used to separate out the tumorous region from the abnormal image. Key Words: Melasma MRI, bounding box, clustering, Morphological operators INTRODUCTION An abnormal growth of the tissue in brain is called brain tumour. This abnormal brain tissue keeps on growing and keep on multiplying without any control. Brain tumour are of metastatic and malignant or benign type. The primary or metastatic is a type of tumour that had come from another cancer cells contained body part to the brain.the brain tissues can be diagnosed by using the scans like Computed Tomography (CT) scans,magnetic Resonance Imaging (MRI) scans,positron Emissions Tomography (PET) scans Biopsy (tissue sample analysis) But Magnetic Resonance Imaging (MRI) is an excellent method which give us high quality images for the check up of the cancer cells.mri image will give us an opportunity to determine the abnormality present in the brain without any risk to patient s health. One of the major stage in the every image processing system is the process of classification. The classification of the given input image should be done under two classes i.e. normal and abnormal class. Classification is done with the help of features contained the tumour containing image and normal image. In feature extraction,the transformation of input image data into sets of features is done. If the precise features are extracted from MRI then the further processing can be done quickly. Feature extraction plays an crucial role in determining the performance of the classifier. For classification of normal and abnormal,ensemble based classification is used[1].after classification the partitioning is performed on the tumorous image for extracting the tumour region. Considering the fact that most of the time MR image have less contrast therefore these segments can be imbricate on eachother.the location of the timorous part in the malignant image is done by bounding box method. LITERATURE SURVEY There are many stages in the image processing system. Initially In the pre-processing stage the MRI image has to be filtered if any noise is present in it.mri image mainly consist of Gaussian noise and this gaussain noise can be filtered by using, mean filter, K-Means filter Bilateral filter, Trilateral filter. But our proposed method is using wiener filter. In the past few years there are many methods used for classification which are mainly of two basic types: a) Supervised learning: Artificial neural network (ANN), Support vector machine (SVM), K - nearest neighbor (KNN) b) Unsupervised learning: k-means clustering, Self organizing Map (SOM). Features are extracted for image classification which is indeed a complex task. The features can be extracted by wavelet transform, decision boundary features, spectral mixtures analysis. Datta et al (2011) introduced color-based VIT Univesity, Tamil Nadu, India Correspondence and Reprint Requests: Abhishek Saxena Received: March 11, 2015 Accepted: March 18, 2015 Published Online: March 28, 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (creativecommons.org/licenses/by/3.0) Conflict of interest: None declared Source of funding: Nil

2 Abhishek Saxena and Suchetha.M segmentation using k-means clustering for brain tumor detection. The developed algorithm shows better result than Canny based edge detection. METHODOLOGY Classification phase The input image is taken and passed through wiener filter, then textual features are extracted such as GLCM features. After that classification of image is done. Database Input Image Weiner Filtering Textual feature extraction Classify Normal Image Thus, normal and abnormal i.e. tumorous image is segmented Locating and Segmentation The classified tumour image is now processed through boundary box algorithm for the location of tumour. Tumorous Image Locating the tumour Edge detection Dilate and fill holes Clear border Segmented tumour Tumorous Image Morphological operations are performed for extracting the tumorous part. After segmenting out the tumour part the area of the tumour is found out. PROPOSED METHOD Pre-processing Stage Most of the time MR images are distorted due to the noise added to the different phases from image acquisition to the transfer of the image into the digital form. So noise removal is necessary part before any further processing on images. Gaussian noise mostly appeared into MR images. Proposed system first adds Gaussian noise in the brain MR images. Wiener filter is used is used for noise removal. Feature Extraction The transformation of an image into its set of features is known as feature extraction. Useful features of the image are extracted from the image for classification purpose. First-order histogram based features Histogram of the image gives summary of the statistical information about the image. So first order statistical information of the image can be obtained using histogram of the image. Direct command is used for the mean, variance,entropy in matlab. Co-occurrence matrix based features Histogram based features are local in nature. These features does not consider spatial information into consideration. So for this purpose gray-level spatial co-occurrence matrix based features are defined which are known as second order histogram based features. Contrast, Correlation, Energy, Homogeneity are found using matlab inbuilt function. Gray-Level Co-occurrence Matrices (GLCMs) based features are extracted in this method. These features are extracted using the following equations: a) Contrast: Ʃ x Ʃ y(x-y) 2 C xy (1) b) Entropy: -Ʃ x Ʃ yc xy logc xy (2) c) Energy: Ʃ x Ʃ y 2 xy (3) d) Homogeneity: Ʃ x Ʃ y cxy (4) 1+[x-y] Problems associated with the co-occurrence matrix methods 1. They require a lot of computation i.e. so many 14 International Journal of Current Innovation Research, Vol. 1, Issue1, pp 13-18, March 2015

3 Locating Brain Tumour and Extracting the Features from MRI Images matrices to be computed. 2. Features are not invariant to rotation or scale changes in the texture. Classification Classification is that the procedure for classifying the input patterns into set of classes. Classification classifies the unknown information samples. Selection of an appropriate classifier needs thought of the many factors like process resources it used, accuracy of the classifier for many datasets, and performance of the formula. Figure 1 shows a system used for Ensemble base classifier for the classification purpose. subscripts indicate whether this histogram is of image I or of template R, and the superscripts denote whether this histogram is computed within the region T(s) or within the region B(s). Figure 2 Finding D from image I, using a reference image R Figure 1 Ensemble based classification This Ensemble base classifier uses support vector machine for classification. This system divides knowledge (the information) set arbitrarily into coaching and testing data. K-fold testing is applied on coaching information. To avoid over fitting hold out testing is applied on testing information. Hold out testing ensures non-overlapping of coaching and testing information. simple linear support vector machine Identifying the location of tumor Figure 2, with a horizontal dotted line drawn at a distance s from the top of the images. Now consider the regions: A 1 (s) = [0,w d] [0, s], and A 2 (s) = [0,w] [s, h t], (4) where w d and h t are respectively the width and the height of both the images I and R. Thus A 1(s) and A 2(s) are the portions of image domain respectively above and below the aforementioned horizontal line. Let E(s) denote the following score function: E(s)=( P I A(s), P R A(s) ) ( P I B(s), P R B(s) ) (5) Where P s denote normalized intensity histograms (probability mass functions of image intensities), the Figure 3 A typical score function plot. The inner product between square roots of two normalized histograms is known as Bhattacharya coefficient (BC),] which is a real number between 0 and 1 that measures the correlation between two histograms. When both normalized histograms are identical, their BC value is 1; whereas once the histograms aren't identical, then BC price is zero. Note that the score function E(s) measures the difference of correlations between the upper histograms and the lower histograms. We therefore expect a high score when the upper histograms match very well, while the lower histograms have high mismatch. On the other hand, a low value of E(s) denotes a low correlation between upper histograms, and a high correlation between lower histograms. Based on these observations, we note that a plot of E(s) vs. s should look like the plot shown in Figure 3. The important observations in Figure 3 are that the plot has three distinct regions increasing, decreasing, then increasing where the decreasing segment begins at l and ends at u, where l and u respectively denotes the lower and the upper bound for the rectangular region D, measuring from top of the image. In fact we can prove these statements rigorously with some mild assumptions about the data, i.e., about the image I and the template R. Essentially, we require that the correlation between the image histogram outside D and the template histogram is much larger than that between the image histogram inside D and the template histogram. International Journal of Current Innovation Research, Vol. 1, Issue 1, pp 13-18, March

4 SIMULATION RESULTS Abhishek Saxena and Suchetha.M MR Image Boundary and line of symmetry Original Image: The image for the proposed system is taken Score plot for vertical direction Score plot for horizontal direction Figure 4 Original Image Added gaussain noise: Gaussian noise is added in the input image Segmentation of tumour Sobel operated In the classified image, sobel operator is used to detect the edge which uses mask as given below Figure 7 Finding a bounding box on brain MRI. Filtered Image: Figure 5 Noisy Image Textual features: Classification of image is done by textual feature so seven features are extracted. I. Root mean square error (RMSE) : II. Mean = III. Entropy= IV. Contrast: V. Correlation: VI. Energy: VII. Homogeneity: Locating the tumor Figure 6 Filtered Image The proposed bounding box algorithm only provides a rough estimate of the abnormal region. However, after finding the bounding box, we can fine tune the segmentation boundary as shown in Figure 7. Note that this operator is placed on associate emphasing pixelsthat ar nearer to the middle of the mask,which made us to choose this operator. Dilation operation To convert the edge detected output to an binary image dilation operation is done in Figure 9. Filled holes Figure 8 Edge detected The binary image is filled with holes wherever empty holes are present 16 International Journal of Current Innovation Research, Vol. 1, Issue1, pp 13-18, March 2015

5 Locating Brain Tumour and Extracting the Features from MRI Images Figure 9 Binary conversion MR images. After that texture features are extracted from these noise free MRI images. These extracted features are used for classification. After classification into normal and tumorous image the location of the tumor in the image is found out. Then edge detection is done using sobel operator,dilated,filled holes in it, cleared out the boundaries and finally segmented out the tumor. Also the area is calculated. FUTURE WORKS In future ensemble based classification will be performed. and segmentation will be done using support vector machine(svm) technique as it is more efficient technique than other segmentation techniques. References Final segmented image After clearing the border image is segmented out with the diamond based mask. Area of tumour Area of objects in binary image is calculated by area an image using inbuilt matlab function, which estimates the area of the objects in binary image. Given image can be numeric(for this input, any pixels having nonzero values are considered to be on) or logical. The results has the class of double. Here the area of tumor = e+03 CONCLUSION Figure 10 Binary image with filled holes Figure 11 Final Segmented tumour The planned system is developed for the diagnosing of tumour from magnetic resonance imaging pictures of the brain. This method makes the diagnosing in many phases.in the preprocessing stage,noise elimination using wiener filter is performed on brain 3. C.A. Cocosco, A.P. Zijdenbos, A.C. Evans, A fully automatic and robust brain MRI tissue classification method, Med. Image Anal. 7 (4) (2003) Christ, M. J., & Parvathi, R. M. S.; Magnetic Resonance Brain Image Segmentation. International Journal of VLSI Design & Communication Systems, Vol.3,No.4, pp , Gopal, N.N.; Karnan, M., "Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques," IEEE International Conference on Computational Intelligence and Computing Research (ICCIC),vol.2, no.3, pp.1-4, Leela G A1, Morphological Approach for the Detection of Brain Tumour and Cancer Cells, Journal of Electronics and Communication Engineering Research Volume 2 ~ Issue 1 (2014) pp: Liu, Wechsler, Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition, IEEE Trans. Image Process 11 (2002) M. C. Jobin Christ; R. M. S. Parvathi Segmentation of Medical Image using K- Means Clustering and Marker Controlled Watershed Algorithm European Journal of Scientific Research, ISSN X, vol.71, no.2, pp , M. Stella, Blair, Fully automatic segmentation of the brain in MRI, IEEE Trans. Med. Imaging 17 (1) (1998) International Journal of Current Innovation Research, Vol. 1, Issue 1, pp 13-18, March

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