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Glestos, M., Mougiakakou, S.G., Matsopoulos, G.K., Nikita, K.S., Nikita, A.S. and Kelekis, D. (2001) Classification of Hepatic Lesions from CT Images Using Texture Features and Neural Networks. Proceedings of 23rd Annual EMBS International Conference, Turkey, 2748-2752.

has been cited by the following article:

• TITLE: A Simple Computational Approach for the Texture Analysis of CT Scan Images Using Orthogonal Moments

  AUTHORS: Nallasivan Gomathinayagam, Janakiraman Subbiah

  KEYWORDS: Orthogonal Moments, CT Scan Images, ROI and Watershed Segmentation, Feature Extraction, Accuracy

  JOURNAL NAME: Circuits and Systems, Vol.7 No.8, June 22, 2016

  ABSTRACT: This paper is a study on texture analysis of Computer Tomography (CT) liver images using orthogonal moment features. Orthogonal moments are used as image feature representation in many applications like invariant pattern recognition of images. Orthogonal moments are proposed here for the diagnosis of any abnormalities on the CT images. The objective of the proposed work is to carry out the comparative study of the performance of orthogonal moments like Zernike, Racah and Legendre moments for the detection of abnormal tissue on CT liver images. The Region of Interest (ROI) based segmentation and watershed segmentation are applied to the input image and the features are extracted with the orthogonal moments and analyses are made with the combination of orthogonal moment with segmentation that provides better accuracy while detecting the tumor. This computational model is tested with many inputs and the performance of the orthogonal moments with segmentation for the texture analysis of CT scan images is computed and compared.