Wavelet Statistical Texture Features with Orthogonal Operators Tumour Classification in Magnetic Resonance Imaging Brain
- 1 , India
Copyright: © 2020 R. Meenakshi and P. Anandhakumar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Tumors medically also called neoplasms are an abnormal mass of tissue resulting from uncontrolled proliferation or division of cells occurring in the human body. If such growth is located in the brain then it is called as brain tumor. Identification of such tumors is a major challenge in the field of medical science. Early identification of tumors prove to be critical as serious consequences can be averted. Its threat level depends on a combination of various factors like the type of tumor, its location, its size and its developmental stage. Tumor can occur in any part of the body. Magnetic Resonance Imaging (MRI) technique is mainly used for analyzing the brain, as the images produced are of high precision and applicability. The main objective of this study is to classify the brain MRI dataset for the existence or non existence of tumors. The proposed method uses Two Dimensional Discrete Wavelet Transform (2D-DWT) for pre-processing and further classification with orthogonal operators and SVM. The usage of 2D-DWT for pre-processing improves the classification accuracy by 2% when compared to the existing classification techniques.
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- Discrete Wavelet Transform
- Support Vector Machine (SVM)
- Orthogonal Polynomial Operator
- Orthonormal Operator