FUZZY C-MEANS BASED MRIMAGE FOR SEGMENTATION BRAIN TUMOR DETECTION USING GRABCUT

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1 FUZZY C-MEANS BASED MRIMAGE FOR SEGMENTATION BRAIN TUMOR DETECTION USING GRABCUT Durga.M 1, Dr.Valarmathi.A 2 P.Gstudent 1, Assistant professor 2 1,2 Department of Computer Application, University college of engineering, Anna University Bit Campus, Trichy, Tamilnadu Abstract Medical image processing is the most challenging and emerging field today. This paper describes the methodology of detection & extraction of brain tumor from patient s MRI scan images of the brain. The segmentation of brain tumors in magnetic resonance images (MRI) is a challenging and difficult task because of the variety of their possible shapes, locations, image intensities. In this Review paper, it is intended to summarize and compare the methods of automatic detection of brain tumor through Magnetic Resonance Image (MRI) used in different stages of analysis. This method incorporates with some noise removal functions, segmentation and morphological operations which are the basic concepts of image processing. This work focuses on edema and tumor segmentation that is based on skull stripping and kernel based fuzzy c-means approach. Clustering process is improved by combining multiple kernel based on the spatial information. Grabcut algorithm is incorporated in this framework as a co-segmentation to identify exact cut point between edema and tumor so that edema is removed from tumor. Detect the tumor is identified and edema properly removed from MRI scan images of the brain is done by using MATLAB Keywords Detection,Morphological Operation,Fuzzy C-means, skull stripping, Grabcut segmentation. 1.INTRODUCTION Magnetic Resonance Imaging (MRI) is widely preferred for analyzing the structure of brain and diagnosing brain related diseases. Secondary type of brain tumor the tumor expansion into the brain results from other parts of the body. Imaging tumors with more accuracy plays pivotal role in the diagnosis of tumors. It involves high resolution techniques like MRI, CT,PET etc. MRI is a important mean for studying the body s visceral structures [2]. MRI is widely used because it gives better quality images of the brain and cancerous tissues compared with the other medical imaging techniques such as X-Ray or Computed Tomography (CT). In this paper, the multilevel segmentation using skull stripping and modified fuzzy c-means clustering is proposed to detect the edema and tumor tissues in segmented brain images. Introductory to this work is provided in this section. The rest of the paper is organized as follows. Detection describes the proposed work using skull stripping, improved fuzzy c-means clustering and grabcut algorithm. Based on MRI technique, white matter and grey matter are clearly identified in studies while analyzing brain structures and brain related information. Brain tumor affected area point value are analysis. White matter and grey matter are the major cerebral tissues in brain structures. MR images provide good spatial resolution with less sensitivity for identifying low abundance molecules. For identifying different regions, organs and anatomical structures from data received via MRI or other medical imaging technique by using the segmentation. To identify affected area and objects in the MR image will be the result of segmentation. This paper presents the technique that can be effectively applied to detect and extract the brain tumor from MRI images by using the fuzzy C-means techniques 2.LITERATURE REVIEW Khotanolou presented automatic segmentation algorithm to detect brain tumor in 3D MRI data. In first phase, initial tumor segment is detected using histogram analysis, morphological operations and symmetry analysis. Then the tumor is detected using fuzzy classification and symmetry analysis again. Their results show that method is effective and suitable for brain tumor detection. Prastawa presented framework [9] for automatic brain tumor segmentation based on outlier detection. At first, abnormalities were detected using information about intensities. Secondary, tumor and edema presence is verified.

2 Havaei presented article [3] about brain tumor segmentation method last year. They implemented deep neural networks with two different types of architectures. First type was two pathway architecture made from two streams. It allowed follow two aspects visual details of the region around that pixel and where the patch is in the brain. Secondary three types of cascade architecture were implemented. Results of the methods are very promising data. Shraddha Dhande 2012 Clustering is a process of partitioning or grouping a given sector unlabeled pattern into a number of clusters such that similar patterns are assigned to a group, which is considered as a cluster. There are two main approaches to clustering which is crisp clustering and fuzzy clustering techniques.one of the characteristic of crisp clustering method is that the boundary between clusters is fully defined but in many real cases the boundary between clusters cannot be clearly defined. Clustering is used for pattern recognition in image processing, and usually requires a high volume of computation. This high volume computation requires considerable amount of memory which may lead to frequent disk access, making the process inefficient. With the development of affordable high performance parallel systems, parallel algorithms may be utilized to improve performance and efficiency of such tasks. The computation speed and memory requirement needed for executing FCM is a big hurdle which tried to overcome in this report. In FCM, the cluster centre initialized by random numbers and it requires more number of iteration for converging to a final actual GC is a popular graph based segmentation for identifying brain tumor in MR images where edges are represented as nodes to find the similarity. Boykov& Jolly presented a GC algorithm for PET and MRI image segmentation where the shrinking problem was not defined properly [21]. Fuzzy c-means clustering (FCM) gets more information from the given image than other hard clustering approaches, however the FCM without considering the spatial information is more sensitive to noise. 3. ALGORITHM OVERVIEW 3.1SEGMENTATION Image segmentation is the method of breaking down an image into small parts.segmentation is performed to make the analysis easier. There are following types of image segmentation. Boundary approach or Thresholding It is the most commonly used segmentation method. It is the gray valve remapping method where if p is considered as an operation then as shown in equation (1), p (v )= {o if v< t 1 if v t...(1) where v is the gray valve and t is the threshold value.in the thresholding method the gray image is converted to binary image. After thresholding the image has segmented into two values 0 and FUZZY C-MEANS Fuzzification of any technique allows partial membership value to each data point to fall in one or more clusters. That is, each pixel is assigned a membership value due to which it can fall under more than one clusters, thereby improving the accuracy of the primitive technique. Member of one fuzzy set can also be the member of other fuzzy sets in the same image. There is no abrupt change between full membership and no membership. The membership function defines the fuzziness of the image and also to define the information EDGE APPROACH In edge-based segmentation method, the detected edges in an image are assumed to represent object boundaries and used to identify these objects. Edge based segmentation very rarely gives the absolute distinct and closed boundaries needed for a direct segmentation. Chances are more that false edge detection and many of the times it requires edge linking to joint the partial edges into an object boundary REGION APPROACH Region based approach depends on the assumption that the bordering pixels within one region have similar values. It focuses on finding object region instead of it's edges.it compares one pixel with its neighbors,if the congruence criteria satisfies then the pixel can be set to belong to the cluster as one or more of its neighbors. Different clustering algorithms are used in this type of approach. A)K-MEANS ALGORITHM K-means algorithm is widely used clustering technique. Which is also known as hard clustering algorithm, it partitions a given dataset into c or k clusters. This algorithm is simple fast and robust to implement. It has some disadvantages as it may not be successful to find overlapping clusters and it also fails to cluster noisy data and nonlinear datasets.

3 B)FUZZY CLUSTERING Fuzzy clustering also known as soft clustering. In this an object is a member of a single cluster as well as a member of many clusters. i.e. objects which are located on the boundaries of the clusters are not forced to belong to a certain cluster, rather they can be member of many clusters. 3.4 FEATURE EXTRACTION Extracting the exact tumor is a crucial task in case of brain tumor because of the complex structure of brain. Certain parameters are taken into account for feature extraction as size, shape, composition, location of the image. As per the results obtained from the feature extraction the classification of the tumor is done. 3.5 GRAYSCALE MORPHOLOGICAL RECONSTRUCTION Grayscale morphological reconstruction is an iterative process. Input for the algorithm is mask image. Actually mask image is the processed image. Algorithm also needs marker image. Grayscale morphological reconstruction is described in detail in the book by Sikudov.In basic morphological reconstruction binary dilation or erosion is applied for the marker image. Then the algorithm calculates the intersection with mask image. 3.6 FILTERING: Filtering is a technique used for eliminating the noise present in an image. The median filter that provides median values of the pixels are used because the mean values obtained using averaging filters results in blurring of the image. In MRI, Gaussian and salt and pepper noise are more predominant. Salt and Pepper noise can be eliminated by median filter, whereas Gaussian noise is eliminated by a Gaussian high pass filter GAUSSIAN HIGH PASS FILTER: It is done to sharpen then image. A high pass filter preserves the high frequency information within an image while reducing the low frequency information, thus emphasizing the transitions in the image intensities MEDIAN FILTER Median filter is very widely used in digital image processing because, under certain conditions, it preserves edge while removing noise. [13] The task of filtering is performed by the median filter by use of window that is a pattern of neighbours. The window pattern slides, entry by entry, over the entire signals. The middle value of the window is decided by the median value of all the entries at a time. 3.7 BOUNDARY REGION MASK Boundary Mask based approach is used to detected in tumor and around from mask with affected area 3.8 GRABCUT SEGMENTATION graph cut algorithm detects boundaries of the tumor. In that case input for the GrabCut method is a mask. Mask marks foreground, probably background and background. Foreground is determined by the mask created after adaptive thresholding. It is because changes of the boundaries should not be large. Probably background is a poly created from the same mask and the rest is background. INPUT: MRI of brain image. OUTPUT: Tumor detect and removed edema of the image. Step 1:- Read the input grayscale image. 4.ALGORITHM FOR DETECTING BRAIN TUMOUR Step 2:- Filters the multidimensional array with the multidimensional filter. We show salt and pepper noise in our image using imnoise command. Now we are reducing the noise in our image using imfilter command i.e. by using median filter and Gaussian filter to get the resultant enhanced image and skull stripping procedure. Step 3:- Initialize cluster centers and maximum iteration 3. Concatenate main image into two bit plane Form bit planes with fuzzy c-means cluster center values Step 4:- Find Euclidean distance (3)

4 Step 5:-Compute membership and assign new cluster centers based on- 4. Step 6:-Calculate fcm for second bit plane as per (5)Fuzzy c-means Cluster image based on threshold values. Display Fuzzy c-means clustered image. Compute the morphological operation by two matlab command imerode and strel with arbitrary shape. Trace region boundaries to get required brain structure Step 7: Grabcut segmentation method apply to removed edema 5.EXISTING SYSTEM Image Processing techniques are used to detect tumor that has mainly following steps PreProcessing, segmentation, Feature Extraction and Classification. The flowchart of the steps followed in tumor detection and classification is shown in figure. Thresholding methods ignore the spatial characteristics and it is not possible to correlate parameters such a mean, standard deviation with different types of tumors after thresholding. FCM based Segmentation 6.PROPOSED SYSTEM To process the data by assigning the partial membership value to each pixel in the image by using fuzzy logic. The membership value ranges from 0 to 1 in the fuzzy set. Intermediate values i.e. a member of one fuzzy set can also be a member of other fuzzy sets in the same image are allowed by Fuzzy clustering. The fuzziness of an image and the information contained in the image is defined by the membership function shown on result FCM Algorithm The M pixels on the image and m the fuzziness value are the input to the algorithm. 2 are used as a fuzziness value on this system. The algorithm contains following steps. a) Initialize u with random values between zero and one. b) Calculate objective function F c) Calculate centroids of the clusters Cj d) Calculate the fuzzy membership table Uij e) Recalculate F f) Go to the centroid of the cluster in step c until a stopping condition was reached.

5 7.ARCHITECTURE 8.EXPERIMENTAL RESULT Implement the algorithm in matlab using some in build function. First Read the gray scale MR image and convert it to the binary form using some function in matlab. To remove noise use the median filter. The images are shown below. Many filters are used to remove the noise from the images. Linear filters can also serve the purpose like Gaussian, averaging filters. For example average filters are used to remove salt and pepper noise from the image. Because in this filter pixel s value is replaced with its neighborhood values. Median filter is also used to remove the noise like salt and pepper and weighted average filter is the variation of this filter and can be implemented easily and give good results Skull Stripping procedure apply to removed from preprocessing brain tumor mrimage

6 output image for Fuzzy C Means. It is mainly developed for the accurate prediction of tumor cells which are not predicted by K-means algorithm. It gives the accurate result for that compared to the K-Means. fuzzy partition of the image by giving each pixel a degree of belonging to a given region. The filtered image is converted to grey in preprocessing and FCM is applied to it gives the segmented tumor identify result Morphological operation shape of tumor erosion is apply on the image. The cell of interest has been successfully segmented tumor apply mask. The GrabCut method from the OpenCV library is used. The method needs input poly or a mask which represents foreground. The rest is background. In this step poly is used as foreground initialization to prevent removal of the brain tumor parts. Initial poly is created depending on the previous thresholding as shown in result 9.CONCLUSION Brain tumor detection and segmentation are done using the Fuzzy C-means algorithm in this research work. For detecting the tumor in the image by using the Fuzzy C-means segmentation method which was applied to MRI scanned image of a human brain. The Better result is given by this technique as compared to previous researchers. Experiments are performed on various images and results were extraordinary. Our proposed research is easy to execute and thus can be managed easily

7 10.REFERENCES 1. S. Leibfarth, F. Eckert, S. Welz, C. Siegel, H. Schmidt, N. Schwenzer,D. Zips, D. Thorwarth, Automatic delineation of tumor volumes by cosegmentation of combined PET/MR data, Physics in Medicine and Biology,60(14) (2015) H. Zaidi, Molecular imaging of small animals, New York, NY, USA: Springer, (2014). 3. Sikka, K., Sinha, N., Singh, P. K., & Mishra, A. K. (2009). A fully automated algorithm under modified FCM framework for improved brain MR image segmentation. Magnetic Resonance Imaging, 27(7), J. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, and A. Yuille, Efficient multilevel brain tumor segmentation with integrated Bayesian model classification, IEEE Trans. Med. Image., vol. 27, no. 5, pp , May J. R. Jiménez-Alaniz, V. Medina-Bañuelos, and O. Yáñez-Suárez, Data-driven brain MRI segmentation supported on edge confidence and a priori tissue information, IEEE Trans. Med. Image., vol. 25, no. 1, pp , Jan K. M. Iftekharuddin, J. Zheng, M. A. Islam, R. J. Ogg, Fractal-based brain tumor detection in multimodal MRI, Applied Mathematics and Computation, vol. 207, pp , W. Dou, S. Ruan, Y. Chen, D. Bloyet, J.-M. Constans, A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images, Image and Vision Computing, vol. 25, pp , N. Zhang, S. Ruan, S. Lebonvallet, Q. Liao, Y. Zhu, Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation, Computer Vision and Image Understanding, vol. 115, pp , H. A. Vrooman, C. A. Cocosco, F. Lijn, R. Stokking, M. A. Ikram, M. W. Vernooij, M. M. B. Breteler, and W. J. Niessen, Multi-spectral brain tissue segmentation using automatically trained k-nearest-neighbor classification, NeuroImage, vol. 37, pp , M. Prastawa, E. Bullitt, S. Ho, G. Gerig, A brain tumor segmentation framework based on outlier detection, Medical Image Analysis, vol. 8, pp , Jul Prakash S (2007) Multiple textured objects segmentation using DWT based texture features in geodesic active contour. Proc IntConfComputIntellMultimedAppl 2: Satheeskumaran, S. and Sabrigiriraj, M., A new LMS based noise removal and DWT based R-peak detection in ECG signal for biotelemetry applications. National Academy Science Letters, 37(4), ppt A. Demirhan, İ. Güler, Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation, Eng. Application. of Artificial Intell., vol. 24, pp , M.N. Do, M. Vetterli, The contour let transform: an efficient directional multire solution image representation, IEEE, Trans. Image Process, 14(12), pp , T. Kohonen, The Self-Organizing Maps, 3rd Edition, Germany: Springer, Krinidis, S., Chatzis, V.: A robust fuzzy local information C-means clustering algorithm, IEEE Trans. Image Process., 2010, 5, (19), pp D. Graves, W. Pedrycz, Fuzzy C-Means, Gustafson Kessel FCM, and Kernel-based FCM: a comparative study, Advances in Soft Computing, 41, , Shi, F., Wang, L., Dai, Y., et al.: Pediatric brain extraction using learning based meta-algorithm, Neuro image, 2012, 62, pp IBSR, The Internet Brain Segmentation Repository, B. Van Ginneken, T. Heimann, M. Styner, 3D Segmentation in the Clinic: A Grand Challenge, 2007, pp C. Rother, V. Kolmogorov, and A. Blake. Grab-Cut : Interactive foreground extraction using iterated grab cuts. ACM Trans. Graph., 23(3): , August.

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