Abstract
An iterative registration algorithm, the Lucas-Kanade algorithm, is combined with the histogram transformation to jointly optimize the spatial registration and the histogram compensation. Based on a simple regression model, a nonparametric estimator, the empirical conditional mean, is used for the histogram transformation function. The proposed algorithm provides a good performance in registering microscopic images that have different exposure or histogram properties, and can easily adopt other histogram compensation schemes and variations of the Lucas-Kanade algorithms due to its implicit flexibility. Joint registration with a third-order polynomial warp and compensation is conducted for microscopic images that have different magnifications.
| Original language | English |
|---|---|
| Pages (from-to) | 3779-3782 |
| Number of pages | 4 |
| Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference |
| Publication status | Published - 2006 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics