Paper Info
Title
Accelerating 3D medical volume segmentation using GPUs.
Abstract
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 5× compared with the sequential version.
Year
DOI
Venue
2018
10.1007/s11042-016-4218-0
Multimedia Tools Appl.
Keywords
Field
DocType
Medical image processing, Fuzzy C-Means (FCM) algorithm, Parallel programming, 3D segmentation
Computer vision,Scale-space segmentation,DICOM,Pattern recognition,Segmentation,Computer science,Fuzzy logic,Image processing,Segmentation-based object categorization,Artificial intelligence,Region of interest,Graphics processing unit
Journal
Volume
Issue
ISSN
77
4
1573-7721
Citations 
PageRank 
References 
22
0.79
41
Authors
5
Name
Order
Citations
PageRank
Mahmoud Al-Ayyoub173063.41
Shadi Alzubi2815.12
Yaser Jararweh396888.95
Mohammed A. Shehab41046.94
Brij Bhooshan Gupta51569.95