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Hemorrhage detection using edge-based contour with fuzzy clustering from brain computed tomography images

Author

Listed:
  • N. S. Bhadauria

    (GBPIET)

  • Indrajeet Kumar

    (Graphic Era Hill University)

  • H. S. Bhadauria

    (GBPIET)

  • R. B. Patel

    (Chandigarh College of Engineering and Technology)

Abstract

The paper presents a segmentation method for extracting the hemorrhage out of CT (computed tomography) images of brain by using the features of fuzzy clustering together with the level-set segmentation method. The fuzzy clustering is utilized for initialization of level-set function that evolves to extract the desired hemorrhagic region. In addition, the fuzzy clustering has also been utilized for estimating the parameters which control the propagation of level set function. The proposed method eradicates the requirement of manual initialization and re-initialization process which is very much time inefficient, as required by majority of conventional level-set segmentation methods and thus speeding up the process related with evolution of function associated with level-set. The proposed method has been implemented over a dataset containing 300 CT images of brain with hemorrhages of various sizes and shapes and the performance of proposed method is compared with existing techniques like fuzzy c- mean (FCM) clustering and region growing. The results of this method are observed to have highest values related with similarity indices such as overlap metric, accuracy, specificity and sensitivity with values as 87.46%, 85.40%, 98.79% and 79.91% respectively for given dataset of 300 images.

Suggested Citation

  • N. S. Bhadauria & Indrajeet Kumar & H. S. Bhadauria & R. B. Patel, 2021. "Hemorrhage detection using edge-based contour with fuzzy clustering from brain computed tomography images," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1296-1307, December.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:6:d:10.1007_s13198-021-01269-7
    DOI: 10.1007/s13198-021-01269-7
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    References listed on IDEAS

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    1. Weiguo Wan & Hyo Jong Lee, 2020. "Deep feature representation and ball-tree for face sketch recognition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(4), pages 818-823, August.
    2. Shefali Arora & M. P. S. Bhatia, 2020. "Presentation attack detection for iris recognition using deep learning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 232-238, July.
    3. Xiaojun Zhang, 2021. "Application of human motion recognition utilizing deep learning and smart wearable device in sports," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 835-843, August.
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