.png){kind=icon}
返回
Accelerating 3D medical volume segmentation using GPUs
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-016-4218-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Medical images have an undeniably integral role in the process of diagnosing and treating of a very large number of ailments. Processing such images (for different purposes) can significantly improve the efficiency and effectiveness of this process. The first step in many medical image processing applications is segmentation, which is used to extract the Region of Interest (ROI) from a given image. Due to its effectiveness, a very popular segmentation algorithm is the Fuzzy C-Means (FCM) algorithm. However, FCM takes a long processing time especially for 3D model. This problem can be solved by utilizing parallel programming using Graphics Processing Unit (GPU). In this paper, a hybrid parallel implementation of FCM for extracting volume object from medical DICOM files has been proposed. The proposed algorithm improves the performance 5x compared with the sequential version.
Keyword:
Medical image processing
Fuzzy C-Means (FCM) algorithm
Parallel programming
3D segmentation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Isolation and characterization of the cell-associated region of group A streptococcal M6 protein
OAAI
OAAI分享收藏Computer-aided diagnosis in medical imaging: Historical review, current status and future potential医学影像中的计算机辅助诊断: 历史回顾，现状和未来潜力OAAI
OAAI分享收藏分享收藏A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images.png)
ACADEMIC RADIOLOGY
ACADEMIC RADIOLOGYIF3.9
PREAI
分享收藏
查看更多内容