Smallest Univalue Segment Assimilating Nucleus approach to Brain MRI Image Segmentation using Fuzzy C-Means and Fuzzy K-Means Algorithms

  • AJALA Funmilola Alaba Computer Science and Engineering Department, Ladoke Akintola University of Technology, Ogbomoso, Oyo State.
  • AKANDE Noah Oluwatobi Computer Science Department, Landmark University, Omuaran, Kwara State.
  • ADEYEMO Isiaka Akinkunmi Computer Science and Engineering Department, Ladoke Akintola University of Technology, Ogbomoso, Oyo State.
  • Ogundokun Roseline Oluwaseun Computer Science Department, Landmark University, Omuaran, Kwara State.
Keywords: Brain magnetic resonance imaging, cluster validity functions, fuzzy C-means, fuzzy K-means, segmentation


Image segmentation still remains an important task in image processing and analysis. Sequel to any segmentation process, preprocessing activities carried out on the images have a great effect on the accuracy of the segmentation task. This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms. Subsequently, brain tissue extraction tool was employed in extracting non-brain tissues from the brain image. Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (FCM) segmentation algorithms were employed for segmenting brain MRI images acquired from four different MRI databases into their White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) constituents. Evaluation metrics such as cluster validity functions using partition coefficients and partition entropy; area error metrics such as false positive, true positive, true negative and false negative (FN); similarity index, sensitivity and specificity were used to evaluate the performance of both techniques. A comparative analysis of the experimental results revealed that in most instances, FKM segmentation technique is preferable to FCM segmentation technique for brain MRI segmentation task.


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