Total views : 198

Performance Evaluation of Parallel Genetic Algorithm for Brain MRI Segmentation in Hadoop and Spark


  • Department of Computer Science, Christ University, Bangalore - 560029, Karnataka,, India
  • IBM Global Center of Excellence, Bangalore, Karnataka,, India


Objectives: The radical growth of brain MRI data demands faster and accurate processing. To meet these demands, it is necessary to develop a design in cloud platform using distributed platforms. Methods/Analysis: In this paper, we introduce an architecture developed for the cloud using Apache Hadoop to segment the brain MRI images. The scanned MRI images are uploaded through either through web interface or mobile app to the system in the public cloud. The Parallel Genetic Algorithm (PGA) in the cloud system enabled with Hadoop or Spark is used to segment the given MRI images. Findings: The processing time taken for different size of data varying from 2GB to 10GB in a different number of clusters varying from one to five are denoted. This process has been implemented in both Apache Hadoop and Apache Spark. The time ranges from 12 to 24 secs approximately in Hadoop whereas the processing time has come down from 4 to 7 secs in Spark. First of all, the results prove that the network based applications for Medical Image Processing are outperformed by the cloud platform applications. Novelty/Improvement: Distributed Platforms have been used in Cloud environment for Brain MRI segmentation using Parallel Genetic Algorithm.


Apache Hadoop, Brain MRI Segmentation, Cloud Computing, Medical Image Processing, Parallel Genetic Algorithm, Spark

Full Text:

 |  (PDF views: 196)


  • Application on Reinforcement Learning for Diagnosis Based on Medical Image. Available from: based_on_medical_image. Date Accessed.01/01/2008
  • Grefenstette JJ. GENESIS: A System for Using Genetic Search Procedures. Proceedings of the 1984 Conference on Intelligent Systems and Machines.1984. p. 161–65.
  • Muhlenbein H, Schomisch M, Born J. The Parallel Genetic Algorithm as Function Optimizer. Genetic Algorithms.1991; 17(7):619–32.
  • Kaghed HN, Al–Shamery SE, Al-Khuzaie FE. Multiple Sequence Alignment based on Developed Genetic Algorithm.Indian Journal of Science and Technology. 2016 Jan; 9(2):1–7.
  • Pettey CC, Leuze MR, Grefenstette J. A Parallel Genetic Algorithm. Proceedings of the ICGA Symp.1987; 155–61.
  • Alba E, Troya JM. A survey of parallel distributed genetic algorithms. John Wiley & Sons, Inc: USA. 1999; 4(31):31–52.
  • Tanese R. Distributed Genetic Algorithms. Proceedings of the 3rd International Conference on Genetic Algorithms.1989. p. 434–39.
  • Shenbagarajan A, Ramalingam V, Balasubramanian C, Palanivel S. Tumor Diagnosis in MRI Brain Image using ACM Segmentation and ANN-LM Classification Techniques. Indian Journal of Science and Technology.2016 Jan; 9(1):1–12.
  • Lin SC, Punch WF, Goodman ED. Coarse-Grain Parallel Genetic Algorithms: Categorization and New Approach.Proceedings of 6thIEEE Symposium on Parallel and Distributed Processing, Dallas, TX. 1994; 28–37.
  • Spiessens P, Manderick B. A Massively Parallel Genetic Algorithm. Proceedings of the 4th International Conference on Genetic Algorithms. 1991. p. 279–86.
  • Gordon VS, Darall Whitley L. Serial and Parallel Genetic Algorithms as Function Optimizers. Proceedings of the 5th International Conference on Genetic Algorithms. 1993. p.177–83.
  • Lozano M. Application of Fuzzy Logic Based Techniques for Improving the Behavior of GAs with Floating Point Encoding, Univ Granada. 1996.
  • Manderick B, Spiessens P. Fine-Grained Parallel Genetic Algorithms. Proceedings of the 3rd International Conference on Genetic Algorithms. 1989. p. 428–33.
  • Mejia-Olvera M, Cantu-Paz E. Dgenesis-Software for the Execution of Distributed Genetic Algorithms. Proceedings of the Latinoamericana de Informatica. 1994. p. 935–46.
  • Marin FJ, Trelles-Salazar O, Sandoval F. Genetic algorithms on LAN message passing architectures using PVM: Application to the routing problem. Proceedings of the International Conference on Evolutionary Computation. 3rd International Conference on Parallel Solving from Nature Parallel Problem Solving from Nature. 1994. p. 534–43.
  • Fan Y, Jinag T, Evans DJ. Volumetric segmentation of brain images using parallel genetic algorithms. IEEE Transactions on Medical Imaging. 2002; 21(8):904–09.
  • Baraiya N, Modi H. Comparative Study of Different Methods for Brain Tumor Extraction from MRI Images using Image Processing. Indian Journal of Science and Technology. 2016 Jan; 9(4):1–5.
  • Chipperfield A, Fleming P. Parallel Genetic Algorithms, Parallel and Distributed Computing Handbook, MacGrawHill: USA. 1996; 1118–43.
  • Jafari M, Shafaghi R. A Hybrid Approach for Automatic Tumor Detection of Brain MRI Using Support Vector Machine and Genetic Algorithm. Global Journal of Science, Engineering and Technology. 2012; 1(3):1–8.
  • Bhatia M, Bansal A, Yadav D, Gupta P. Proposed Algorithm to Blotch Grey Matter from Tumored and Non Tumored Brain MRI Images. Indian Journal of Science and Technology. 2015 Aug; 8(17):1–10.
  • Li W, Huang Y. A Distributed Parallel Genetic Algorithm oriented adaptive migration strategy. 2012 Eighth International Conference on Natural Computation (ICNC).2012. p. 592–95.
  • Sasirekha N, Kashwan KR. Improved Segmentation of MRI Brain Images by Denoising and Contrast Enhancement.Indian Journal of Science and Technology. 2015 Sep; 8(22):1–7.
  • Narayana AGH, Krishnakumar U, Judy MV. An Enhanced Map Reduce Framework for Solving Protein Folding Problem using a Parallel Genetic Algorithm.ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India. 2014; 1:241–50.
  • Eizadpanah E, Koroupi F. Timing of Resources in Cloud Computing by using Multi-Purpose Particles Congestion Algorithm. Indian Journal of Science and Technology. 2015 Apr; 8(S8):1–10.
  • Peter DA. Enhancing the Efficiency of Parallel Genetic Algorithms for Medical Image Processing with Hadoop. International Journal of Computer Applications. 2014; 108(14):11–6.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.