We propose a method for improving image quality in medical images by using a wavelet-based approach. The proposed method integrates two components: image denoising and image enhancement. In the first component, a modified undecimated discrete wavelet transform is used to eliminate the noise. In the second component, a wavelet coefficient mapping function is applied to enhance the contrast of denoised images obtained from the first component. This methodology can be used not only as a means for improving visual quality of medical images but also as a preprocessing module for computer-aided detection/diagnosis systems to improve the performance of screening and detecting regions of interest in images. To confirm its superiority over existing state-of-the-art methods, the proposed method is experimentally evaluated via 30 mammograms and 20 chest radiographs. It is demonstrated that the proposed method can further improve the image quality of mammograms and chest radiographs, as compared to two other methods in the literature. These results reveal the effectiveness and superiority of the proposed method. 1. Introduction Denoising and contrast enhancement operations are two of the most common and important techniques for medical image quality improvement. Because of their importance, there has been an enormous amount of research dedicated to the subject of noise removal and image enhancement [1–4]. With regard to image denoising, some approaches using discrete wavelet transform (DWT) have been proposed [5–7]. The DWT is very efficient from a computational point of view, but it is shift variant. Therefore, its denoising performance can change drastically if the starting position of the signal is shifted. In order to achieve shift invariance, researchers have proposed the undecimated DWT (UDWT) [8–10]. Mencattini et al. reported a UDWT-based method for the reduction of noise in mammographic images [11]. The reported method was robust and effective. However, the method was not advantageous in terms of computational aspects. Zhao et al. proposed an image denoising method based on Gaussian and non-Gaussian distribution assumptions for wavelet coefficients [12]. Huang et al. reported on a denoising method which involves directly selecting the thresholds for denoising by evaluating some statistical properties of the noise [13]. Recently, Matsuyama et al. proposed a modified UDWT approach to mammographic denoising [14]. The results demonstrated that the method could further improve image quality and decrease image processing time. As regard to the improvement of
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