Abstract
Ultrasound's images are generally affected by speckle noise which is mainly due to the scattering phenomenon’s coherent nature. Speckle filtration is accompanied with loss of diagnostic features. In this paper a modest new trial introduced to remove speckles while keeping the fine features of the tissue under diagnosis by enhancing image’s edges; via Curvelet denoising and Wavelet based image fusion. Performance evaluation of our work is done by four quantitative measures: the peak signal to noise ratio (PSNR), the square root of the mean square of error (RMSE), a universal image quality index (Q), and the Pratt’s figure of merit (FOM) as a quantitative measure for edge preservation. Plus Canny edge map which is extracted as a qualitative measure of edge preservation. The measurements of the proposed approach assured its qualitative and quantitative success into image denoising while maintaining edges as possible. A Gray phantom is designed to test our proposed enhancement method. The phantom results assure the success and applicability of this paper approach not only to this research works but also for gray scale diagnostic scans’ images including ultrasound’s B-scans.
Highlights
Ultrasound medical imaging which has been widely accepted as an essential safe tool for biological tissue medical diagnosis, are generally affected by speckle noise due to the scattering phenomenon’s coherent nature
Optimizing the Applied Fusion Method: Our goal is to find the better performance of the applied fusion method that makes maximum edge preservation as well as maintains image's quality and peak signal to noise ratio (PSNR) as possible
The results was good but after being optimized; it became better; so, we satisfied here only by displaying the final average quantitative results of the 20 different results for: PSNR, root of the mean square of error (RMSE), Q, and figure of merit (FOM), in comparison by the same average results after being optimized by the suitable fusion ratio based on applying S&M index enhancement, see Table I
Summary
Ultrasound medical imaging which has been widely accepted as an essential safe tool for biological tissue medical diagnosis, are generally affected by speckle noise due to the scattering phenomenon’s coherent nature. Speckle noise is a well known phenomenon inherent most B-mode ultrasonic scans’ images caused by the constructive and destructive interferences of the wavelets scattered by the tissue components as they arrive at the transducer [1], [2]. Speckle noise poses a well known problem in ultrasound imaging [4]. It acts as a mask of the small differences in grey level images [5]. The pre filtering process of Speckle noise cannot be avoided. It is a critical pre-processing step, providing clinicians with enhanced diagnostic ability [13]. The filtration is accompanied with loss of diagnostic features The amount of these losses differs according to the techniques reported so far
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