IMAGE SEGMENTATION METHODS FOR BRAIN MRI IMAGES

Swapnali Matkar, Megha Borse

Abstract: In Image Processing, extracting the region of interest is a very challenging task. To extract information, pre-processing algorithms are important in MRI image. Edge detection is a task in which points in image are identified at which brightness changes sharply or it has discontinuities. It is an essential pre- processing step in medical image segmentation, for object recognition of the human organs. The applications of medical image segmentation are 3D reconstruction and quantitative analysis and so on. We used MRI images because MRI images give best view of tissues in any part of human body. In this paper, difficulties of edge detection in brain magnetic resonance images are considered and a new approach to edge detection is introduced. There are many traditional edge detection methods for extracting edges from images have been introduced such as gradient based operators like sobel, prewitt, robert were initially used for edge detection, but they did not give sharp edges and were highly sensitive to noise image. And in medical field accuracy is important fact. To overcome these difficulties, we proposed new method called as Active Contour method or snake model. . In the field of medical segmentation, Active contour method is one of popular research topic. This method is used for detecting brain region based on their energy function. In order to compare between them, one slice of MRI image tested with these methods. The traditional and proposed edge detection algorithms are implemented in MATLAB and results of proposed method are presented and compared with traditional approach.

Keywords: Edge detection, Brain MRI images, Canny edge detector, Active contour method and MATLAB.

DOI: https://doi.org/10.15623/ijret.2015.0403045