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.