Abstract
Classifying images of HEp-2 cells from indirect immunofluorescence has important clinical applications. We have developed an automatic method based on random forests that classifies an HEp-2 cell image into one of six classes. The method is applied to the data set of the ICPR 2012 contest. The previously obtained best accuracy is 79.3% for this data set, whereas we obtain an accuracy of 97.4%. The key to our result is due to carefully designed feature descriptors for multiple level sets of the image intensity. These features characterize both the appearance and the shape of the cell image in a robust manner.
| Original language | English |
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| Title of host publication | Pattern Recognition (ICPR), 2012 21st International Conference on |
| Publisher | IEEE - Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 4 |
| ISBN (Print) | 978-1-4673-2216-4 |
| Publication status | Published - 2012 |
| Event | 21st International Conference on Pattern Recognition (ICPR 2012) - Tsukuba, Japan Duration: 2012 Nov 11 → 2012 Nov 15 |
Conference
| Conference | 21st International Conference on Pattern Recognition (ICPR 2012) |
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| Country/Territory | Japan |
| City | Tsukuba |
| Period | 2012/11/11 → 2012/11/15 |
Subject classification (UKÄ)
- Computer Vision and Robotics (Autonomous Systems)
- Mathematics