Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery
| Autor: | B.S. Manjunath, Zhiqiang Bi, Neal R. Harvey, Laura E. Boucheron, David L. Rimm |
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| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
Multispectral image
Breast Neoplasms Biology Sensitivity and Specificity Multispectral pattern recognition Constant false alarm rate Pattern Recognition Automated 03 medical and health sciences 0302 clinical medicine Image Interpretation Computer-Assisted Humans Breast lcsh:QH573-671 030304 developmental biology Cell Nucleus 0303 health sciences Likelihood Functions business.industry lcsh:Cytology Research Histological Techniques Pattern recognition Spectral bands Cell Biology Image Enhancement Cell biology 030220 oncology & carcinogenesis Principal component analysis RGB color model Artificial intelligence Detection rate business Image type Algorithms |
| Zdroj: | BMC Cell Biology, Vol 8, Iss Suppl 1, p S8 (2007) BMC Cell Biology |
| ISSN: | 1471-2121 |
| Popis: | Background We present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420–700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis. Results For classification using all available image bands, we find the best performance (equal tradeoff between detection rate and false alarm rate) is obtained from either the multispectral or our "ccd" RGB imagery, with an overall increase in performance of 0.79% compared to the next best performing image type. For classification using single image bands, the single best multispectral band (in the red portion of the spectrum) gave a performance increase of 0.57%, compared to performance of the single best RGB band (red). Additionally, red bands had the highest coefficients/preference in our classifiers. Principal components analysis of the multispectral imagery indicates only two significant image bands, which is not surprising given the presence of two stains. Conclusion Our results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery. |
| Databáze: | OpenAIRE |
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