Document Type : Research Paper


Electrical Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.


Improve medical image visualization is a critical preliminary step before further imagery processing like analyzing texture, extracting features, and segmentation. Imagery noises in medical images are frequently occurred as a consequence of different artificial processes such as acquisition, sending and receiving, and storing & retrieving processes. As a result, the quality of image visualization is degraded. Therefore, a de-noise process is important in order to maintain good image quality for medical purposes. In this paper, medical image enhancement aims to de-noise as much as possible while maintaining detailed features and edges. This work employed an optimization algorithm called "Bat" to enhance the quality of the medical images and also compare it with other methods such as Gaussian filter, median filter, and bilateral and Wiener filter. Obtained image quality was evaluated using range of reference metrics, like, peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM), and signal to noise ratio (SNR). Bat algorithm achieved the best PSNR, SNR, MSE, SSIM values compared to other filters. Findings presented in this research showed that the PSNR performance of the proposed method is (60.6, 55.6, 64.9, 63.6), MSE is (1.125, 1.43, 2.95, 1.15), Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise on order.


  • This work proposes the Bat algorithm to de-noise the medical image with different types of noise.
  • Use several types of noise such as Gaussian noise, salt & pepper noise, speckle noise.
  • Bat algorithm achieved the best results in noise removal and enhance medical images.
  • The proposed Bat optimization image de-noising achieves better RMSE and PSNR values than other techniques (Gaussian filter, median filter, bilateral filter, and wiener filter).
  • Observed the PSNR performance of the proposed method (60.6, 55.6, 64.9,63.6) Gaussian noise, salt-and-pepper noise, speckle noise, and Poisson noise on order


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