Fusion and Segmentation of Abdominal Cancerous Images

  • Authors

    • Nischitha .
    • Padmavathi K
    https://doi.org/10.14419/ijet.v7i3.34.19468
  • Fusion, Curvelet transform, MSVD, Multimodal images, K means clustering, Mean shift segmentation, Normalized cut segmentation.
  • In the field of medicine, multimodal image analysis is attaining importance due to the fact that large number of images with clinical data has to be examined to analyze different types of results. Fusion of multimodal images merges required details from a single or multiple images into a solitary image. Fusion provides increased clinical applicability of medical images which aids in the diagnosis of diseases. Segmentation of fused images will help in identifying meaningful objects in an image based on the problem being solved. Multimodal image fusion is carried using curvelet transform and MSVD (Multi-resolution Singular Decomposition) method. The fused images are segmented using various segmentation techniques such as K means clustering, Mean shift segmentation and Normalized cut segmentation. The performance of various segmentation methods are analyzed using different metrics such as entropy, PSNR (Peak Signal to Noise Ratio) and RMSE (Root Mean Square Error).

     

     
  • References

    1. [1] Pappachen and B. V Dasarathy, “Medical image fusion: A Survey of the State of the Artâ€, Information Fusion, Vol. 19, 2014, pp. 4-19.

      [2] Bjoern H. Menze, Andras Jakab, and Stefan Bauer, “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)â€, IEEE Transactions on Medical Imaging, Vol.34, pp. 1993-2024, 2015.

      [3] Arnaldo Mayer, and Hayit Greenspan, “An Adaptive Mean-Shift Framework for MRI Brain Segmentationâ€, IEEE Transactions On Medical Imaging, Vol. 28, pp. 1238-1250, 2009.

      [4] Sweta Mehta, and Prof. Bijith Marakarkandy, “CT and MRI Image Fusion using Curvelet Transformâ€, Journal of Information, Knowledge and Research in Electronics and Communication Engineering, vol. 2, Issue 2, pp. 848-852, 2014.

      [5] Sugandhi Vij, Dr. Sandeep Sharma, and Chetan Marwaha, “Performance Evaluation of Color Image Segmentation using K Means Clustering and Watershed Techniqueâ€, International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2013.

      [6] Julio Carballido-Gamio, Serge J. Belongie, and Sharmila Majumdar, “Normalized Cuts in 3-D for Spinal MRI Segmentationâ€, IEEE Transactions on Medical Imaging, Vol. 23, pp. 36-44, January 2004.

      [7] Padmavathi K, Dr. Maya V Karki, and Mahima Bhat, “Medical Image Fusion of Different Modalities Using Dual Tree Complex Wavelet Transform with PCAâ€, International Conference on Circuits, Control, Communication and Computing, 2016.

      Aakanksha Bahri and Savita Shiwani, “Improved performance of Image Fusion by MSVDâ€, International Journal of Innovative Science and Research Technology, Vol. 1, Issue 6, September 2016.
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  • How to Cite

    ., N., & K, P. (2018). Fusion and Segmentation of Abdominal Cancerous Images. International Journal of Engineering & Technology, 7(3.34), 752-757. https://doi.org/10.14419/ijet.v7i3.34.19468