Implementation of K-Means Clustering and Fuzzy C-Means Algorithm for Brain Tumor Segmentation

Abstract

Detection of Brain tumor is the most common fatality in the current scenario of health care society. Computational applications are gaining significant importance in the day-to-day life. Specifically, the usage of the computer-aided systems for computational biomedical applications has been explored to a higher extent. Automated brain disorder diagnosis with MR images is one of the specific medical image analysis methodologies. Image segmentation is used to extract the abnormal tumor portion in brain. This paper explores a method to identify tumor in brain disorder diagnosis in MR images and deals with the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images. Most Research in developed countries show that the number of people who have brain tumors were died due to the fact of inaccurate detection. This work uses computer aided method for segmentation (detection) of brain tumor based on the k.means and fuzzy c-means algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis.

Keywords: Abnormalities, Magnetic Resonance Imaging (MRI), Brain tumor, Pre-processing, K-means, fuzzy c-means , Thresholding