Name
Title | ||
|---|---|---|
Fast and Automatically Adjustable GRBF Kernel Based Fuzzy C-Means for Cluster-wise Coloured Feature Extraction and Segmentation of MR Images. |
Abstract | ||
|---|---|---|
Fuzzy C-means algorithm is a popular image segmentation algorithm and many researchers in the past have introduced several improved versions of it. However, they still lacked robustness for segmenting key regions of magnetic resonance (MR) human brain transversal images such as white matter, grey matter, and cerebro spinal fluid with an almost similar contouring of the edges. This study highlights... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1049/iet-ipr.2017.1102 | IET Image Processing |
Keywords | Field | DocType |
biomedical MRI,fuzzy set theory,image colour analysis,image segmentation,medical image processing,radial basis function networks | Normalization (image processing),Computer vision,Pattern recognition,Similarity measure,Segmentation,Euclidean distance,Metric (mathematics),Image segmentation,Feature extraction,Artificial intelligence,Cluster analysis,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 4 | 1751-9659 |
Citations | PageRank | References |
1 | 0.36 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kishorjit Nongmeikapam | 1 | 19 | 6.68 |
| Wahengbam Kanan Kumar | 2 | 1 | 1.37 |
| Aheibam Dinamani Singh | 3 | 2 | 1.38 |