IndexAbstractIntroductionIi. Fcm segmentation (fuzzy C mean)Iii. Dwt (Discrete Wavelet Transform)Iv. Median filterV. Svm (Support Vector Machine) classifierVI. Proposed AlgorithmVii. Simulation ResultsViii. Conclusion Abstract brain tumor can be detected using a computer-based image processing algorithm. An MRI was performed to find the brain tumor. MRI images are not sufficient to accurately diagnose cancer. Fuzzy c mean algorithm is very popular image segmentation. The output of the fuzzy c mean algorithm also contains some unwanted parts. In our proposed work, these unwanted parts can be removed using median filter. In the proposed work, DWT with SVM is used to identify tumor types, whether benign or malignant. The median filtered image also helps improve detection by the SVM classifier. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay KeywordsFCM (Fuzzy C Mean), MRI (Magnetic Resonance Imaging), DWT (Discrete Wavelet Transform), SVM (Support Vector Machines), image segmentation, grayscale image, MRI (magnetic resonance imaging), computed tomography ( CT), image pre-processing, image filtering. Introduction Brain tumor can be detected by various brain scanning techniques. CT scan provides the detailed image of the brain and MRI test where the computer is connected to a strong magnetic field which provides a clear 2D image of the brain. MRI (Magnetic Resonance Imaging) discards radiation unlike CT [2,4]. The MRI image offers a complete view of the brain, and an expert must perform a proper inspection to find the tumor, which makes the process slower and more expensive. To solve this problem, computer-based segmentation algorithms were created. These algorithms provide the tumor as the output image. The most commonly used segmentation is the Fuzzy C Mean (FCM) segmentation algorithm. The FCM algorithm provides accurate results for datasets that are overlapping and is much more efficient than the k-means algorithm [1]. Brain tumors can be classified as benign and malignant. A benign tumor is one that does not grow suddenly. It never affects nearby tissues and does not spread to other parts at all. Malignant tumor is one that worsens with the passage of time and ultimately proves fatal. We can say that malignancy is a tumor in a descriptive or advanced stage from which it is completely impossible to go back [4]. To extract features from the MRI brain image, the Wavelet transform is effective as it allows image analysis at different motion levels suited to its multi-resolution diagnostic properties [1]. To differentiate the type of brain tumor, a Support Vector Machines (SVM) classifier is commonly used. The SVM model represents points in space that are mapped so that examples of separate categories are divided by as wide a clear gap as possible. In our proposed work, FCM algorithm is used for brain MRI image segmentation. The segmented image is further improved using median filter. Here the median filter removes unwanted segmented parts by treating them as noise. The segmentation output is then fed to the DWT and SVM classifier to correctly identify the tumor type.Ii. Fcm (Fuzzy C Mean) Segmentation Fuzzy c-means can be defined as a suboptimal segmentation method that forgoes global optimality to improve performancestatistics and adaptability of the segmentation process. The computational evaluation on FCM is determined by the amount of image points that need to be highly processed at each iteration [5]. FCM is a clustering technique that allows information to belong to two or more clusters [6]. The main aspect of this algorithm works by assigning membership values ​​to each data point resulting in each cluster center based on the distances between the cluster and the data point, the higher the membership value therefore the closer the data is to the cluster center . Clearly, the sum of the members of each data point should equal one [10]. The FCM algorithm is an iterative clustering method that produces an optimal c-partition by minimizing the weight within the group sum of the squared error objective function (JFCM) [8].( 1)Where,X = { x1, x2 , ..., xn} ≤ R,n = number of data,c = number of clusters with 2 ≤ c < n,uik = degree of belonging of xk to the ith cluster, q = weighting exponent on each fuzzy membership,vi = prototype of the cluster center i,d2(xk,vi) is a distance measure between the object xk and the cluster center vi. An object function solution (JFCM) can be calculated by an iterative process, which is as follows: First set the values ​​for q, c, & e, Second, the fuzzy partition matrix needs to be initialized, Third, it is necessary set the loop counter such that b = 0, Compute c cluster centers { vi(b)} with U(b)(2) Compute membership U(b+1), For k = 1 an, compute how much follows:Ik={i|1<=i<=cdik=||xk -vi||=0},~Ik={1,2,……c}-Ik, for the kth column of the matrix, calculates i new membership values ​​and, if Ik=Ø , then(3)else uik(b +1) = 0 for all iє~Ik and ƩiєIk uik(b+1) =1, next k [9],if ||Ub -U(b+1)|| <Ɛ, stop; otherwise set b=b+1 and go to step 4. For medical image segmentation, the suitable clustering type is fuzzy-based clustering. Fuzzy c-means (FCM) can be considered as the fuzzified version of the k-means algorithm. It is a kind of clustering algorithm that allows the data to have a degree of membership in each cluster based on the degree of membership [6].Iii. Dwt (Discrete Wavelet Transform) The wavelet gives the idea of ​​the different frequencies of an image using different scales. DWT provides the wavelet coefficient from MR images of the brain. Two-dimensional DWT provides four subbands, namely LL (low-low), HL (high-low), LH (low-high), HH (high-high) with two-level wavelet decomposition of the region of interest (ROI). The wavelet approximations at the first and second levels are represented by LL1, LL2 respectively; which represents the low frequency part. The high frequency part of the images is represented by LH1, HL1, HH1, LH2, HL2 and HH2 which provide the details of the horizontal, vertical and diagonal directions at the first and second levels, respectively as shown in fig. 1 below [2].Iv. Median FilterMedian filter is very popular in image filtering. It acts as a low-pass filter that blocks all high-frequency components of images such as noise and edges, thus blurring the image [11]. Filtering high-density corrupted images requires a large window so that there are a sufficient number of noise-free pixels in the window. So the size of the sliding window in the median filter varies depending on the noise density. Median filters of window sizes 3×3, 5×5, 7×7 and 9×9 are mainly applicable. The output of the median filter is given by y(i,j)=median{x(is,jt),x(i,j)/(s,t)∈W,(s,t)≠(0,0 )} (4)where {x} is the noisy image and y(i,j) is the recovered image with preserved edges.V. Support Vector Machine (SVM) Classifier The SVM classifier is applied in the.