Statistical analysis of optical coherent tomography image by segmentation methods using euclidean distant model

  • Authors

    • R J. Hemalatha
    • Mohandass G
    • Hari Krishnan G
    2018-05-03
    https://doi.org/10.14419/ijet.v7i2.25.16572
  • Segmentation, Multidimensional Scaling (MDS), Euclidean Distance Model, Statistical Image Analysis.

  • Abstract

    Optical Coherence Tomography (OCT) is a diagnostic measure of the images in retinal layers. The measure of retinal layers is done by image segmentation algorithm. In this work, algorithm of different threshold based segmentation techniques is computed in OCT image. Using the resultant image, comparison is done between the segmentation methods by statistical parameters such as noise factor of the signal peak, Normalized Absolute Error (NAE) and Average Difference (AD). Statistical analysis values are evaluated by multidimensional scaling (MDS) methods. By applying clustering methods in the multidimensional data, the Euclidean Distance model gives the relation distant between the algorithms. By this result, similarity or dissimilarity of segmentation methods was categorized.

     

     

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  • How to Cite

    J. Hemalatha, R., G, M., & Krishnan G, H. (2018). Statistical analysis of optical coherent tomography image by segmentation methods using euclidean distant model. International Journal of Engineering & Technology, 7(2.25), 121-124. https://doi.org/10.14419/ijet.v7i2.25.16572

    Received date: 2018-07-30

    Accepted date: 2018-07-30

    Published date: 2018-05-03