Ultrasound Image Enhancement using Super Resolution

Abstract

Within today's day and age, amongst women who fall under the bracket of being of reproductive age, when discussion about Gynecologic tumors comes forward, Uterine Fibroids are found to be abundant. Since early detection for tumors is statutory for ensuring recovery, ultrasound detection remains prominent for being one of the most convenient Image-capturing methodologies. Although given the convenience of the method, the quality of the images could be deemed sub-optimal, a need to enhance the image arises for successful detection by appropriate algorithms. This all facilitates the development of a technique with a harmonious combination of filters and Super-Resolution Convolutional neural network (SRCNN).Super Resolution technique in itself is altered to make it feasible for monochromatic medical images, which essentially highlights the contagious aspect, i.e., the tumor region of the image. Thus, firstly the captured noisy image is denoised by making use of Spatial filters, which ensures denoising while preserving the essential edges. Then, this output of the filtered image is treated as an input for the Super-resolution technique, as described above. Within the said technique, multiple low-resolution images are combined in Super-Resolution to regenerate a target input image with higher resolution and quality. Furthermore, Subjective, quantitative, and visual execution evaluations were conducted using Peak Signal-to-Noise-Ratio (PSNR) for quantitative and Universal Quality Index (UQI) and structural similarity index metric (SSIM). Upon completion, these results of comparing the quality of super-resolved images on various data sets are provided in numerical form.These comparisons state that the experimental results have shown a substantial improvement in the results by the fusion technique of hybrid filters with Super Resolution convolutional neural network, for instance, the PSNR value achieved is in the range of 40% to 50%. As a result, the proposed technique has demonstrated outstanding performance in all cases, as evidenced by the findings that the proposed algorithms are robust and ultrasound images taken under various lighting conditions and parameters work efficiently and without egregious errors.