@article{MAKHILLAJIT2006545115,
    title = {Intelligent Fractured Image Retrieval From Medical Image Databases},
    journal = {Asian Journal of Information Technology},
    volume = {5},
    number = {4},
    pages = {448-453},
    year = {2006},
    issn = {1682-3915},
    doi = {ajit.2006.448.453},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2006.448.453},
    author = {H. Khanna Nehemiah,A. Kannan and},
    keywords = {Content based retrieval,IFIR,indexing,feature extraction,image database},
    abstract = {The retrieval of stored medical images matching an input medical image is an imperative form of
content-based retrieval. For efficient similarity image retrieval and integration, the medical images should be
processed systematically to extract a representing feature space vector for each member image. This study
explains a system, which takes a fractured image as a query image and retrieves the similar images from the
image database using distance metrics and also provides the radiologists with details about the type of fracture
and the treatment recommended. The key objective of present research is to retrieve similar X-ray images of
fractured reports using K-Nearest Neighbor. Images are matched using color in gray level and texture attributes.
Similarity between images is established based on the respective numeric values (Signature). Features are
extracted from X-ray images. Indexing is also performed on extracted features using a k-d tree data structure for
images and is stored in a backend database for effective retrieval.}
    }