UDC 621 SEGMENTING MRI BRAIN TUMOR IMAGES AND ITS ANALYSIS © Anita Joshi, Graphic Era Hill University, Dehradun, Uttarakhand, India Rahul Chauhan, Graphic Era Hill University, Dehradun, Uttarakhand, India Deepak Singh Rana, Graphic Era Hill University, Dehradun, Uttarakhand, India Abstract. <...> This paper is based on the identification analysis on the output of noisy and filtered images by using image processing techniques. <...> Here we detect the tumor, segment the tumor and calculate the area of tumor. <...> MRI brain tumor images allow obtaining the useful key indication of disease progression. <...> The tumor detection is essential phase to solve the segmentation problem successfully. <...> This paper first pre-process the MRI images, then segment the images to get the tumor information and then calculate the tumor area. <...> For segmenting the images morphological operations such as erosion and dilation is performed. <...> Thresholding the image gives the better result analysis along with morphological operations. <...> There are four basic steps that is required for brain tumor detection. <...> Second stage is to Pre-process the image using image enhancement techniques. <...> LITERATURE SURVEY A) Pre-Processing Stage This Stage is to make the better image quality and enhance from noise, corruption and interference. <...> This stage uses low pass spatial filter, high pass spatial filter (less sharper), high pass spatial filter (more sharper), median filter, prewitt derivative filter. 1. <...> Median filter: the median filter block replaces the central value of an M-by-N neighborhood with its median value. <...> If the neighborhood has a center element, the block places the median value there. 2. <...> Low-pass spatial filter: a low-pass filter, also called a “blurring” or “smoothing” filter, averages out rapid changes in intensity. <...> The simplest low-pass filter just calculates the average of a pixel and all of its eight immediate neighbors. <...> The result replaces the original value of the pixel. <...> The process is repeated for every pixel in the image. 3. <...> High-pass spatial filter: a high-pass filter can be Fig. 1 106 Engineering and Automation Problems, № 2 – 2017 used to make an image appear sharper. <...> These filters emphasize fine <...>