Analysis of Brain Tumor Using Morphological and Histogram

Abstract

This article is presented to show the analysis and detection of brain tumors and help in the analysis of the tumor. The experiment is done on the image processing method which provides accuracy for brain tumor detection. The method which is implemented in this paper is the Histogram technique which is to evaluate the pixel value present in the image. The technique consists of converting images into grayscale images. On these converted images we apply the histogram equalization method. Histogram Equalization is an image processing method that adjusts the contrast of an image by using its histogram. To enhance the image's variations, it spreads out the most recurrent pixel intensity values or stretches out the intensity range of the image. The second method is the Thresholding method in which in image processing, thresholding is the simplest method of segmenting images. From a grayscale image, which is used to create binary images. The simplest thresholding methods replace each pixel in an image with a black pixel if the image intensity is less than some fixed constant T or a white pixel if the image intensity is greater than that constant. In the thresholding method, we converted the images from color or grayscale into a binary image, i.e., one that is simply black and white. Most frequently, we use thresholding to select areas of interest of an image, while ignoring the parts we are not concerned with. Morphological processing which is used a broad set of image processing operations that process images based on shapes. Morphological operations apply a structuring element to an input image, creating an output image of the same size. In a morphological operation, each pixel in the image is adjusted based on the value of other pixels in its neighborhood. By choosing the size and shape of the neighborhood, you can construct a morphological operation that is sensitive to specific shapes in the input image.