Name
Abstract | ||
|---|---|---|
Image mining is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Very few people have systematically investigated this field. Clustering medical images is an important part in domain-specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we firstly quantify the domain knowledge about brain image (especially the brain symmetry), and then incorporate this quantified measurement into the clustering algorithm. Our algorithm contains two parts: (1) clustering regions of interest (ROI) detected from brain image; (2) clustering images based on the similarity of ROI. We apply the method to cluster brain images and present results to demonstrate its usefulness and effectiveness. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.amc.2006.06.083 | Applied Mathematics and Computation |
Keywords | Field | DocType |
clustering region,image domain,image mining,brain symmetry,medical image clustering,domain knowledge,object clustering,medical image,image clustering,cluster brain image,incorporating domain knowledge,clustering image,brain image,clustering algorithm,domain-specific application image mining,region of interest,brain imaging,data mining | Data mining,Similitude,Clustering high-dimensional data,Domain knowledge,Correlation clustering,Consensus clustering,Contextual image classification,Cluster analysis,Mathematics | Journal |
Volume | Issue | ISSN |
185 | 2 | Applied Mathematics and Computation |
Citations | PageRank | References |
10 | 0.66 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiwei Pan | 1 | 52 | 21.31 |
| Jianzhong Li | 2 | 3196 | 304.46 |
| Wei Zhang | 3 | 33 | 5.07 |