Abstract
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.
Highlights
Image processing is a developing field, with novel applications that develop at a fast-growing pace
The performance of developed algorithms/methods is compared to the current methods regarding peak signal-to-noise ratio (PSNR), Mean Square Error (MSE), structural similarity index measure (SSIM), and signal to noise ratio (SNR) (Signal to Noise Ratio)
This work proposed Bat optimization for denies the medical image that achieves by the first step generation the initial population by applying the different filter with different properties, apply select the global best and local best of participation, update the population according to the Bat algorithm and update the global best and local best enhancement image as shown in algorithm one and Figure (1)
Summary
Image processing is a developing field, with novel applications that develop at a fast-growing pace. Digital imagery is a computerized-simulated field with expanded applications ranging from the entertainment industry to the space program [1]. The fascinating characteristics of such a revolution of information can be referred to as the capability of sending and receiving complex data that goes beyond usually written texts. The transmission of visual information in the shape of a digital image becomes one of the most important means of communicating in the twenty-first century [2]. The objective of this research is de-noising images based on the Bat algorithm to increase the visual quality and keep the edge of medical images and build the adaptive model on the different types of medical images.
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