An Advanced Technique of De-Noising Medical Images using ANFIS
Rupinderpal Singh1, Pankaj Sapra2, Varsha Verma3
1Er.Rupinderpal Singh, Computer Science, Punjab Technical University, CT Institute of Engineering & Management, Jalandhar (Punjab) India.
2Er.Pankaj Sapra, Computer Science, Punjab Technical University, CT Institute of Engineering & Management, Jalandhar (Punjab) India.
3Prof. Varsha Verma, Computer Science Punjab Technical University, CT Institute of Engineering & Management, Jalandhar (Punjab) India.
Manuscript received on August 05, 2013. | Revised Manuscript received on August 11, 2013. | Manuscript published on August 15, 2013. | PP: 75-80 | Volume-1 Issue-9, August 2013. | Retrieval Number: I0427081913/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Noise reduction has been a traditional problem in image processing. Medical images like X-RAY, CT, MRI, PET and SPECT have minute information about heart, brain, nerves etc. These images are corrupted during transmission. When these are corrupted by noise, it is impossible to rescue a human being from harmful effects. Recent wavelet thresholding based denoising methods proved promising, since they are capable of suppressing noise while maintaining the high frequency signal details. However, the local space-scale information of the image is not adaptively considered by standard wavelet thresholding methods. In this thesis, a new type of technique neural network and fuzzy has been proposed. The proposed technique confiscates the Additive white Gaussian Noise from the CT images and improves the quality of the CT images. The proposed work is comprised of three phases; they are preprocessing, training and testing. In the preprocessing phase, the CT image which is affected by the AWGN noise is transformed using multi wavelet transformation. In the training phase the obtained multi-wavelet coefficients are given as input to the Neural Network and Fuzzy System. In the testing phase, the input CT image is examined using this trained Neural Network and Fuzzy System and then to enhance the quality of the CT image thresholding is applied and then the image is reconstructed. Hence, the denoised and the quality enhanced CT images are obtained in an effective manner.
Keywords: Image processing, filters, de-noising, Discrete Wavelet Transform(DCT), neural network, fuzzy logic.