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.
Highlights
A tumor is the solid lesion which is produced by an abnormal cells growth which seems to be like swelling
The Results of the inputs both normal and abnormal Computer Tomography (CT) scan images which are given as sample inputs and the intermediate outputs are received as presented
It is observed that the orthogonal moments both discrete and continuous are showing a better feature extraction even in medical images too
Summary
A tumor is the solid lesion which is produced by an abnormal cells growth which seems to be like swelling. The advancement in the medical imaging technologies like Computer Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasonography has significantly raised the accuracy in diagnosing any abnormality in human organs [1]. Even though these technologies are utilized, still there should be more statistical analysis to get the accuracy related to performance analysis. The classification of texture using Zernike moment feature set was proposed by V.S. bharathi et al [7] who proposed the significance test to select the best moment orders to discriminate normal and abnormal tissues in liver images. Racah Moment, one of the discrete orthogonal polynomials was used for the image analysis stated by Hongqing Zhu et al [8]. The performance evaluation of various segmentation methods was analysed by Maheswari et al [11]
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