INVESTIGATION ON CLUSTERING ALGORITHMS IN MRI IMAGES

Abstract

• Brain tumor is an accumulation of abnormal cells in the brain. Detection of tumor from magnetic
  resonance imaging (MRI) brain scan is one of the most promising research topics in medical image
  processing. This paper presents a novel tumor detection system in MRI images using k-means technique
  integrated with Fuzzy c-means (FCM) clustering algorithm and artificial neural network (ANN). ANN is
  used to classify the MRI images into two categories; normal and tumor image. The proposed system takes
  benefit of both integrated algorithms in the aspect of minimal computation time and accuracy. The
  method proposed in this paper is fuzzy c-means (FCM) and is compared with K-Means segmentation.
  Followed by tumor detection which will display the presence of a tumor as Abnormal brain whereas the
  absence of tumor as a normal brain. In the different clusters obtained, it shows different elements of the
  brain such as white matter, gray matter, edema and CSF (Cerebrospinal fluid) and tumor. A user-friendly
  environment is created by using GUI in MATLAB which in turn saves the precious time of doctor to
  diagnose the tumor automatically. Performance analysis using various parameters such as PSNR, Global
  Consistency error (GCE), Variation of Information (VOI), area, elapsed time, reproducibility, and Rand
  Index (RI) is done.