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Brain Tumor Detection and Skull Fracture Detection using Morphological Operators in MATLAB

Author(s):

Shrawan Kumar , Sagar Institute of Research Technology and Science; Nitesh Kumar, Sagar Institute of Research Technology and Science

Keywords:

MRI, Threshold Segmentation, Median Filter, Morphological Operator, Skeleton and Edge Detection

Abstract

This paper presents a technique for the detection of brain tumor and skull fracture from magnetic resonance imaging (MRI). The MRI is basically used in the biomedical to detect, analyze and visualize finer details in the internal structure of the human body. This methodology is basically used to detect the differences in the tissues which have a far better technique as compared to computed tomography (CT). So this method makes this technique a very special one for brain tumor and skull fracture detection. Computed tomography (CT) images which are used to ascertain the difference in tissues density and magnetic resonance imaging (MRI) provides an excellent contrast between various tissues of the body. In this present methodology, preprocessing includes image resizing, conversion to gray, image enhanced in the way that finer details are improved and the noise level removed from the specimen image. In this methodology, the technique is using threshold segmentation, threshold segmentation is one of the simplest segmentation methods which is using for the improves the quality of result. The input gray scale image is converts in a binary format. Here we use one more segmentation method watershed segmentation; watershed segmentation is one of the most appropriate methods to group pixels of an image on the basis of their intensities. The main purpose of morphological operation is to separate the tumor parts and the fracture parts of the image. Now only the tumor and fracture portion of the images is visible, shown as white color and bubble, with the help of edge detection.

Other Details

Paper ID: IJSRDV7I30805
Published in: Volume : 7, Issue : 3
Publication Date: 01/06/2019
Page(s): 1298-1303

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