TY - JOUR
T1 - Algorithm for Optimization in Medical Image Processing applied in Heterogeneous Architecture
AU - Auccahuasi, Wilver
AU - Meza, Sandra
AU - Porras, Emelyn
AU - Reyes, Milagros
AU - Linares, Oscar
AU - Rojas, Karin
AU - Inciso-Roja, Miryam
AU - Pando-Ezcurr, Tamara
AU - Aiquipa, Gabriel
AU - Nicolas-Roja, Yoni
AU - Auccahuasi, Aly
N1 - Publisher Copyright:
© 2022 CEUR-WS. All rights reserved.
PY - 2022
Y1 - 2022
N2 - In these times of pandemic, hospitals are being the focus of many innovations, not only for the adaptation to telemedicine, but also from the perspective of the use and processing of the multiple modalities of medical images, where we find images made up of a single Image such as x-rays, images that are made up of a sequence of images such as tomography and Magnetic Resonance, or in video format as is the case with ultrasound and angiography. One way of working with images is through popular image servers that connect to medical equipment for transfer and storage. In the process of visualization and processing, special workstations with good computational capacity are required for these purposes, in most cases these workstations are connected in the network of medical offices, therefore they are presented in a normal working image display requests at the same time. The methodology presented uses a heterogeneous architecture based on CPU and GPU, in such a way that by means of an algorithm it analyzes the type and dimension of the image to be able to choose where the processing will be carried out, thereby optimizing the use of computational resources. and we can achieve a parallel job that the CPU and GPU are working simultaneously with different imaging modalities. As a result, we present the execution mode of the algorithm where it automatically chooses what type of image is processed by the CPU and what type is processed in the GPU, as well as the execution time in each of them. Finally we can indicate that the algorithm can be scalable towards workstations to optimize its use in clinical practice.
AB - In these times of pandemic, hospitals are being the focus of many innovations, not only for the adaptation to telemedicine, but also from the perspective of the use and processing of the multiple modalities of medical images, where we find images made up of a single Image such as x-rays, images that are made up of a sequence of images such as tomography and Magnetic Resonance, or in video format as is the case with ultrasound and angiography. One way of working with images is through popular image servers that connect to medical equipment for transfer and storage. In the process of visualization and processing, special workstations with good computational capacity are required for these purposes, in most cases these workstations are connected in the network of medical offices, therefore they are presented in a normal working image display requests at the same time. The methodology presented uses a heterogeneous architecture based on CPU and GPU, in such a way that by means of an algorithm it analyzes the type and dimension of the image to be able to choose where the processing will be carried out, thereby optimizing the use of computational resources. and we can achieve a parallel job that the CPU and GPU are working simultaneously with different imaging modalities. As a result, we present the execution mode of the algorithm where it automatically chooses what type of image is processed by the CPU and what type is processed in the GPU, as well as the execution time in each of them. Finally we can indicate that the algorithm can be scalable towards workstations to optimize its use in clinical practice.
KW - GPU
KW - Programming
KW - algorithms
KW - medical imagining
KW - methodology
UR - http://www.scopus.com/inward/record.url?scp=85168878339&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85168878339
SN - 1613-0073
VL - 3445
SP - 33
EP - 43
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2022 Algorithms, Computing and Mathematics Conference, ACM 2022
Y2 - 29 August 2022 through 30 August 2022
ER -