Noise removal of the x-ray medical image using fast spatial filters and GPU

Luis Cadena; Alexander Zotin; Franklin Cadena; Nikolai Espinosa

doi:10.1117/12.2319327

17 September 2018 Noise removal of the x-ray medical image using fast spatial filters and GPU

Luis Cadena, Alexander Zotin, Franklin Cadena, Nikolai Espinosa

Proceedings Volume 10752, Applications of Digital Image Processing XLI; 107521R (2018) https://doi.org/10.1117/12.2319327
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

Medical images are corrupted by different types of noises caused by the equipment itself. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from images is now a very challenging issue in the field of medical image processing. This work undertake the study of noise removal techniques in medical image by using fast implementation of different digital filters, such as average, median and Gaussian filter. Processing of X-ray medical images takes a significant time. Now days modern hardware allows to use parallel technology for image processing on CPU and GPU. Using GPU processing technology were proposed parallel implementations of noise reduction algorithm taking into account the data parallelism. The experimental study conducted on medical X-ray image, so that to choose the best filters considering medical task and time of processing. The comparison of the implementation of fast filters algorithm and GPU implementation show great increase in performance. Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms.

Citation Download Citation

Luis Cadena, Alexander Zotin, Franklin Cadena, and Nikolai Espinosa "Noise removal of the x-ray medical image using fast spatial filters and GPU", Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107521R (17 September 2018); https://doi.org/10.1117/12.2319327

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