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

A new computational solution to compute the uptake index from 99mTc-MDP bone scintigraphy images

The appearance of bone metastasis in patients with breast or prostate cancer makes the skeleton most affected by metastatic cancer. It is estimated that these two cancers lead in 80% of the cases to the appearance of bone metastasis, which is considered the main cause of death. 99mTc-methylene diphosphonate (99mTc-MDP) bone scintigraphy is the most commonly used radionuclide imaging technique for the detection and prognosis of bone carcinoma. With this work, it was intended to develop a new computational solution to extract from 99mTc-MDP bone scintigraphy images quantitative measurements of the affected regions in relation to the non-pathological regions. Hence, the uptake indexes computed from a new imaging exam are compared with the indexes computed from a previous exam of the same patient. Using active shape models, it is possible to segment the regions of the skeleton more prone to be affected by the bone carcinoma. On the other hand, the metastasis is segmented using the region-growing algorithm. Then, the uptake rate is calculated from the relation between the maximum intensity pixel of the metastatic region in relation to the maximum intensity pixel of the skeletal region where the metastasis was located. We evaluated the developed solution using scintigraphic images of 15 patients (7 females and 8 males) with bone carcinoma in two distinct time exams. The bone scans were obtained approximately 3 h after the injection of 740 MBq of 99mTc-MDP. The obtained indexes were compared against the evaluations in the clinical reports of the patients. It was possible to verify that the indexes obtained are according to the clinical evaluations of the 30 exams analyzed. However, there were 2 cases where the clinical evaluation was unclear as to the progression or regression of the disease, and when comparing the indexes, it is suggested the progression of the disease in one case and the regression in the other one. Based on the obtained results, it is possible to conclude that the computed indexes allow a quantitative analysis to evaluate the response to the prescribed therapy. Thus, the developed solution is promising to be used as a tool to help the technicians at the time of clinical evaluation

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

Uso de técnicas de paralelização em GPGPU e multirresolução para a suavização de imagem

Around the world, a lot of researchers have been developed unnumbered contributions to the image processing area, among them, methods have been proposed to reduce the required time to processing large images or achieve real time processing. In this work, it is combined GPGPU parallelization and multiresolution techniques to reducing the processing time required to removing multiplicative noise in images. The experiments have revealed very promising results regarding the achieved computacional speedup

New computational solution to compute the uptake index from 99mTc-MDP bone scintigraphy images

The appearance of bone metastasis in patients with breast or prostate cancer makes the skeleton most affected by metastatic cancer. It is estimated that these two cancers lead in 80% of the cases to the appearance of bone metastasis, which is considered the main cause of death. 99mTc-methylene diphosphonate (99mTc-MDP) bone scintigraphy is the most commonly used radionuclide imaging technique for the detection and prognosis of bone carcinoma. With this work, it was intended to develop a new computational solution to extract from 99mTc-MDP bone scintigraphy images quantitative measurements of the affected regions in relation to the non-pathological regions. Hence, the uptake indexes computed from a new imaging exam are compared with the indexes computed from a previous exam of the same patient. Using active shape models, it is possible to segment the regions of the skeleton more prone to be affected by the bone carcinoma. On the other hand, the metastasis is segmented using the region-growing algorithm. Then, the uptake rate is calculated from the relation between the maximum intensity pixel of the metastatic region in relation to the maximum intensity pixel of the skeletal region where the metastasis was located. We evaluated the developed solution using scintigraphic images of 15 patients (7 females and 8 males) with bone carcinoma in two distinct time exams. The bone scans were obtained approximately 3 h after the injection of 740 MBq of 99mTc-MDP. The obtained indexes were compared against the evaluations in the clinical reports of the patients. It was possible to verify that the indexes obtained are according to the clinical evaluations of the 30 exams analyzed. However, there were 2 cases where the clinical evaluation was unclear as to the progression or regression of the disease, and when comparing the indexes, it is suggested the progression of the disease in one case and the regression in the other one. Based on the obtained results, it is possible to conclude that the computed indexes allow a quantitative analysis to evaluate the response to the prescribed therapy. Thus, the developed solution is promising to be used as a tool to help the technicians at the time of clinical evaluation