Brain tumor segmentation based on a hybrid clustering technique

Abstract Image segmentation refers to the process of partitioning an image into mutually exclusive regions. It can be considered as the most essential and crucial process for facilitating the delineation, characterization, and visualization of regions of interest in any medical image. Despite intensive research, segmentation remains a challenging problem due to the diverse image content, cluttered objects, occlusion, image noise, non-uniform object texture, and other factors. There are many algorithms and techniques available for image segmentation but still there needs to develop an efficient, fast technique of medical image segmentation. This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.

[1]  Ali Ahsan,et al.  A New Approach to Image Segmentation for Brain Tumor detection using Pillar K-means Algorithm , 2013 .

[2]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[3]  S. Islam,et al.  Implementation of Image Segmentation for Natural Images using Clustering Methods , 2013 .

[4]  A. Kandaswamy,et al.  IMPROVED HYBRID SEGMENTATION OF BRAIN MRI TISSUE AND TUMOR USING STATISTICAL FEATURES , 2010 .

[5]  Cheng G. Weng,et al.  A New Evaluation Measure for Imbalanced Datasets , 2008, AusDM.

[6]  Tuhin Utsab Paul,et al.  Automatic Segmentation of Brain Tumour from Multiple Images of Brain MRI , 2013 .

[7]  D. Donoho,et al.  Does median filtering truly preserve edges better than linear filtering , 2006, math/0612422.

[8]  Aichi Chien,et al.  Frame based segmentation for medical images , 2011 .

[9]  Gurjeet kaur Seerha.  Review on Recent Image Segmentation Techniques , 2013 .

[10]  A AjalaFunmilola,et al.  Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation , 2012 .

[11]  Varsha Kshirsagar,et al.  Segmentation of Brain Tumour and Its Area Calculation , 2014 .

[12]  Manas Ranjan Patra,et al.  SOME CLUSTERING ALGORITHMS TO ENHANCE THE PERFORMANCE OF THE NETWORK INTRUSION DETECTION SYSTEM , 2008 .

[13]  Gauri Anandgaonkar,et al.  Brain Tumor Detection and Identification from T 1 Post Contrast MR Images Using Cluster Based Segmentation , 2014 .

[14]  Julia Punitha Malar Dhas,et al.  An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab , 2014 .

[15]  R. Raja,et al.  Spatial Fuzzy C Means PET Image Segmentation of Neurodegenerative Disorder , 2013, ArXiv.

[16]  Stefan Holban,et al.  Segmentation of bone structure in X-ray images using convolutional neural network , 2013 .

[17]  Kaushal Doshi,et al.  A Study of Segmentation Methods for Detection of Tumor in Brain MRI , 2014 .

[18]  Sarada Dakua,et al.  Use of chaos concept in medical image segmentation , 2013, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[19]  B.R.Shyamala Devi,et al.  Intelligent Based Brain Tumor Detection Using ACO , 2013 .

[20]  José M. Bioucas-Dias,et al.  Denoising of medical images corrupted by Poisson noise , 2008, 2008 15th IEEE International Conference on Image Processing.

[21]  D. Naik,et al.  A Review on Image Segmentation Clustering Algorithms , 2014 .

[22]  Sunil Agrawal,et al.  Integration of Clustering, Optimization and Partial Differential Equation Method for Improved Image Segmentation , 2012 .

[23]  V. Janani,et al.  IMAGE SEGMENTATION FOR TUMOR DETECTION USING FUZZY INFERENCE SYSTEM , 2013 .

[24]  Cheolha Pedro Lee,et al.  Robust Image Segmentation using Active Contours: Level Set Approaches , 2005 .

[25]  Amit Yerpude,et al.  Colour image segmentation using K – Medoids Clustering , 2012 .

[26]  Swapna Devi,et al.  Image Segmentation Techniques , 2012 .

[27]  Maitreyee Dutta,et al.  Image Segmentation for Uneven Lighting Images using Adaptive Thresholding and Dynamic Window based on Incremental Window Growing Approach , 2012 .