Lung Cancer Diseases Diagnostic Asistance Using Gray Color Analysis

    CIMSiM 2010 : Second International Conference on Computational Intelligence, Modelling and Simulation

    Bali, Sept 28-30, 2010

    Ford Lumban Gaol


    Errors in diagnosing the disease is a critical risk that must be faced by any person giving treatment to the hospital. Medical treatment can not always be done with perfect accuracy. Lung cancer is one of the most deadly disease that prone to misdiagnose. In general, some practitioners tend to read cancer in x-ray rontgen image as tumor this could be
    fatal. To generate a diagnose, a general practitioner use three kind of examination i.e : patient History, Radiologic examination, phisical examination. In this paper, Gray color
    for image indexing and retrieval are investigated. The features are derived based on the statistical distribution of Harralick feature from image sample. By utilizing the proposed
    invariant features, the similarity measure between query and database images provides reliable retrieval results.