Noise Cancellation in Computed Tomography Images through Adaptive Multi-Stage Noise Removal Paradigm

Image de-noising is a noise removal approach, which is utilized to remove noise from the noisy image and is utilized to protect the significant features of images namely, corners, edges, textures, and sharp structures. For medical diagnosis Computer tomography (CT) images are mainly utilized. Due to acquisition and transmission in CT imaging, the noise that appears leads to poor image quality. To overcome this problem, an efficient Noise cancellation in computed tomography images using adaptive multi-stage noise removal paradigm is proposed. The proposed approach consists of three phases namely, Optimal Discrete Wavelet Transform, first stage noise removal using Block Matching, and 3D filtering (BM3D) filter and second stage noise removal using the bilateral filter (BF). Initially, Discrete Wavelet Transform (DWT) is applied to the input image to diminish noise in CT images. In this method, co-efficient ranges are optimally selected with the help of Crow Search Optimization (CSO) algorithm. Secondly, to remove the noise present in the bands, BM3D algorithm is applied. Finally, bilateral filter is applied to the BM3D output image to further enhance the image. The performance of the proposed methodology is analyzed in terms of Peak signal-to-noise ratio (PSNR), Root Mean Square Error (RMSE), and Structural Similarity Index (SSIM). Furthermore, the multi-stage noise removal model obtained gives the best PSNR values compared to other techniques.

@article{Subash2021,
  title     = {Noise Cancellation in Computed Tomography Images through Adaptive Multi-Stage Noise Removal Paradigm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Jenita Subash and Kalaivani S},
  doi       = {10.14569/IJACSA.2021.0121221},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121221}
}