Efficient parallelization on GPU of an image smoothing method based on a variational model

Medical imaging is fundamental for improvements in diagnostic accuracy. However, noise frequently corrupts the images acquired, and this can lead to erroneous diagnoses. Fortunately, image preprocessing algorithms can enhance corrupted images, particularly in noise smoothing and removal. In the medical field, time is always a very critical factor, and so there is a need for implementations which are fast and, if possible, in real time. This study presents and discusses an implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on graphics processing units techniques. The use of these techniques facilitates the quick and efficient smoothing of images corrupted by noise, even when performed on large-dimensional data sets. This is particularly relevant since GPU cards are becoming more affordable, powerful and common in medical environments

Método de suavização de imagem baseado num modelo variacional paralelizado em arquitetura CUDA

O aumento constante da velocidade de cálculo dos processadores tem sido uma grande aliada no desenvolvimento de áreas da ciência que exigem processamento de alto desempenho. Associado ao aumento dos recursos computacionais, tem-se presenciado um aumento no emprego de técnicas de computação paralela, no intuito de explorar ao máximo a capacidade de processamento das arquiteturas multiprocessador. No entanto, o custo financeiro para aquisição de hardware para computação paralela não é baixo, implicando assim aa busca de alternativas. A arquitetura GPGPU (General Purpose Computing on Graphics Processing Unit), torna-se uma opção de baixo custo sem comprometer o poder de processamento necessário. Neste trabalho, esta arquitetura é empregada na paralelização de um método de suavização de imagem baseado num modelo variacional, aplicado em sequências de imagens de ultra-sonografia. Os resultados obtidos são promissores, permitindo um ganho de tempo computacional considerável

Techniques of Medical Image Processing and Analysis accelerated by High-Performance Computing: A Systematic Literature Review

Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome