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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Janoos, K. Irfanoglu, M.O. Mosaliganti, K. Machiraju, R. Huang, K. Wenzel, P. deBruin, A. Leone, G. |
| Copyright Year | 2007 |
| Description | Author affiliation: Comput. Sci. & Eng., Ohio State Univ., Columbus, OH (Janoos, K.; Irfanoglu, M.O.; Mosaliganti, K.; Machiraju, R.; Huang, K.; Wenzel, P.; deBruin, A.; Leone, G.) |
| Abstract | Recently, the 2-point correlation functions (2-pcfs) were employed in building feature vectors for histological image segmentation. The 2-pcfs serve as estimators of material distributions with respect to the component packing in a multi-phase sample. The multi-phase properties estimated by the 2-pcfs were represented in a tensor structure and a HOSVD-based classification algorithm was developed. In this paper, we employ a multi-resolution framework in the image and the 2-pcfs feature scale-space, in order to achieve significant savings in computational costs. We also propose a new formulation of the HOSVD classifier that learns the relative skew in the feature space. The classifier helps in improving the segmentation accuracy. Our improved results are validated against ground-truth generated from large histology images of mouse placenta. |
| Starting Page | 300 |
| Ending Page | 303 |
| File Size | 100904 |
| Page Count | 4 |
| File Format | |
| ISBN | 1424406714 |
| DOI | 10.1109/ISBI.2007.356848 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Tensile stress Image resolution Computational efficiency Biological materials Mice Computer science Biomedical engineering Genetic engineering Biomedical informatics |
| Content Type | Text |
| Resource Type | Article |
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