Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Orthogonal moments such as Zernike moments and Legendre moments have been proven to have superior feature representation capability and low information redundancy. The number of orthogonal moments to be used as features or numerical attributes to perform any application is minimal due to the orthogonal nature. However, the information possessed by each moment order needs to be analysed to identify the appropriate moment orders for the undertaken task. In this work, a statistical significance test has been performed to select the best moment orders to discriminate normal and abnormal tissues in liver images. The experimental results reveal the efficacy of the proposed features. Keywords: Orthogonal moments, Texture, Feature Selection, Classifier