A novel method for segmenting brain tumor using modified watershed algorithm in MRI image with FPGA

The goal of the segmentation of brain images is to separate the images in different non-compatible homogenous areas reflecting the numerous anatomical structures. Brain segmentation by magnetic resonance has numerous implications for diagnosing brain disorganizations such as Alzheimer's, Parkinson-related syndrome among others. However, it is not an simple job to automatically segment the MR image. The main motive of this study is to provide a better segmentation approach for the segment of the ROI (Region Of Interest) region from the MRI image by solving the issues that currently exist in the literary works. MRI segmentation is not a trivial task, because acquired MR images are imperfect and are often corrupted by noise and other image artifacts. The variety in technologies for image processing has contributed to the creation in numerous image segmentation techniques. That is because there is no universal approach, nor are all methods necessarily appropriate for a specific form of picture suitable for all pictures. Other approaches still use the gray level histogram, for example, while others integrate detailed spatial picture details for bleeding conditions. Some methods use statistical techniques, but some do incorporate existing information to enhance segmentation efficiency. Some methods utilize probabilistic or fuzzy methods. Yet there are certain inconveniences of all the current processes. Therefore, we have intended to propose a new segmentation approach for the ROI region segmentation. The proposed work comprised of three phases namely preprocessing, edge detection and segmentation. At first, the MRI images are extracted from the database and that each of the input images is enhanced by applying a high pass filter. After completing the preprocessing method, the enhanced canny edge detection (ECED) approach is used to enhance the image. After that, the images are given to the modified watershed segmentation (MWS) algorithm which separates the ROI part from MRI Image. The testing consequences demonstrate that the proposed system accomplishes to give the good result related to the available strategies. Xilinx Virtex-5 FPGA is used to implement in this paper.