Name
Abstract | ||
|---|---|---|
Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.5121/ijaia.2018.9203 | International Journal of Artificial Intelligence & Applications |
Field | DocType | Volume |
Canny edge detector,Pattern recognition,Lesion,Edge detection,Segmentation,Computer science,Fuzzy logic,Multiple sclerosis,Artificial intelligence,Cluster analysis,Magnetic resonance imaging | Journal | abs/1804.03282 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saba Heidari Gheshlaghi | 1 | 0 | 0.68 |
| Abolfazl Madani | 2 | 0 | 0.34 |
| Amir Abolfazl Suratgar | 3 | 0 | 0.68 |
| Fardin Faraji | 4 | 0 | 0.34 |