Name
Abstract | ||
|---|---|---|
In this work, an interactive visual system MICS is presented for large-scale brain CT image classification. Automatic feature extraction algorithms are added in MICS to improve system efficiency and classification accuracy. In visualization part, we designed an interactive feature extraction interface, enable users to extract and fine-tune image features according to specific requirements. In addition, all image features in database are visualized as dynamic charts in every phase of classification. These allow users to compare the current image with others in some specific feature and re-mark the possible misclassification. Finally, by series experiments and case studies, we verify the performance of the classification algorithm as well as the effec-tiveness and applicability of the visual design in MICS. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) | medical image, classification system, process visualization, ideal midsagittal line |
Field | DocType | ISSN |
Computer vision,Communication design,Feature extraction algorithm,Pattern recognition,Noise measurement,Feature (computer vision),Visualization,Computer science,Feature extraction,Artificial intelligence,Contextual image classification,Machine learning | Conference | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenbo Li | 1 | 112 | 9.31 |
| Haiwei Pan | 2 | 52 | 21.31 |
| Xiaoqin Xie | 3 | 18 | 10.36 |
| Zhiqiang Zhang | 4 | 114 | 25.82 |
| Han Qilong | 5 | 0 | 0.34 |