Name
Abstract | ||
|---|---|---|
Comparing staining patterns of paired antibodies designed towards a specific protein but toward different epitopes of the protein provides quality control over the binding and the antibodies' ability to identify the target protein correctly and exclusively. We present a method for automated quantification of immunostaining patterns for antibodies in breast tissue using the Human Protein Atlas database. In such tissue, dark brown dye 3,3'-diaminobenzidine is used as an antibody-specific stain whereas the blue dye hematoxylin is used as a counterstain. The proposed method is based on clustering and relative scaling of features following principal component analysis. Our method is able (1) to accurately segment and identify staining patterns and quantify the amount of staining and (2) to detect paired antibodies by correlating the segmentation results among different cases. Moreover, the method is simple, operating in a low-dimensional feature space, and computationally efficient which makes it suitable for high-throughput processing of tissue microarrays. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1155/2014/647273 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Field | DocType | Volume |
Epitope,Computer vision,3,3'-Diaminobenzidine,Computer science,Image segmentation,Tissue Array Analysis,Artificial intelligence,Staining,Pathology,Antibody | Journal | 2014 |
ISSN | Citations | PageRank |
1748-670X | 1 | 0.36 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jimmy C. Azar | 1 | 3 | 1.93 |
| Martin Simonsson | 2 | 1 | 0.36 |
| ewert bengtsson | 3 | 135 | 25.36 |
| Anders Hast | 4 | 1 | 0.36 |