Name
Abstract | ||
|---|---|---|
Parallel programming has many benefits that can help developers and researchers to improve the performance of some algorithms to become more efficient in real life. This is especially true for systems involving medical images. Image segmentation for volume extraction is a famous segmentation process that takes long time to finish execution. In this paper, we consider a new version of the Fuzzy C-Means (FCM) segmentation algorithm (known as IT2FPCM) and provide a parallel implementation of it that is 12X time faster than the sequential implementation. The considered algorithm is based on Interval Type-2 FCM and combines fuzzy and possibilistic ideas in order to obtain higher accuracy. We conduct our experiments using two different machines and the results show that the improvement gains for both machines 11X and12X, respectively. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE/ACS 13TH INTERNATIONAL CONFERENCE OF COMPUTER SYSTEMS AND APPLICATIONS (AICCSA) | Scale-space segmentation,Segmentation,Computer science,Fuzzy logic,Segmentation-based object categorization,Image segmentation,Linear programming,Artificial intelligence,Cluster analysis,Machine learning |
DocType | ISSN | Citations |
Conference | 2161-5322 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shadi Alzubi | 1 | 81 | 5.12 |
| Mohammed A. Shehab | 2 | 104 | 6.94 |
| Mahmoud Al-Ayyoub | 3 | 730 | 63.41 |
| Elhadj Benkhelifa | 4 | 238 | 37.76 |
| Yaser Jararweh | 5 | 968 | 88.95 |