Optimal Features Selection for X-Ray Image Retrieval with Regular and Irregular Zoning Methods

  • Authors

    • P. Nalini
    • Dr B. L. Malleswari
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.20017
  • Region Based Retrieval, Zoning Methods, Intensity attributes, Statistical attributes, SVD.

  • Abstract

    Medical Image Retrieval is mainly meant for enhancing the healthcare system by coordinating physicians and interact with computing machines. This helps the doctors and radiologists in understanding the case and leads to automatic medical image annotation process. The choice of image attributes have crucial role in retrieving similar looking images of various anatomic regions.  In this paper we presented an empirical analysis of an X-Ray image retrieval system with intensity, statistical features, DFT and DWT transformed coefficients and Eigen values using Singular Valued Decomposition techniques as parameters. We computed these features by dividing the images in five different regular and irregular zones. In our previous work we proved that analyzing the image with local attributes result in better retrieval efficiency and hence in this paper we computed the attributes by dividing the image into 64 regular and irregular zones. This experimentation carried out on IRMA 2008 and IRMA 2009 X-Ray image data sets. In this work we come up with some conclusions like wavelet based textural attributes, intensity features and Eigen values extracted from different regular zones worked well in retrieving the images over the features computed over irregular zones. We also determined like the set of image features in which form of zoning for different anatomical regions  result in excellent retrieval of  similar looking X-Ray images.



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  • How to Cite

    Nalini, P., & B. L. Malleswari, D. (2018). Optimal Features Selection for X-Ray Image Retrieval with Regular and Irregular Zoning Methods. International Journal of Engineering & Technology, 7(4.5), 87-90. https://doi.org/10.14419/ijet.v7i4.5.20017

    Received date: 2018-09-22

    Accepted date: 2018-09-22

    Published date: 2018-09-22