DocumentCode :
2475306
Title :
Grading nuclear pleomorphism on histological micrographs
Author :
Cosatto, Eric ; Miller, Matt ; Graf, Hans Peter ; Meyer, John S.
Author_Institution :
NEC Labs., Princeton, NJ, USA
fYear :
2008
fDate :
8-11 Dec. 2008
Firstpage :
1
Lastpage :
4
Abstract :
A mainstay in cancer diagnostics is the classification or grading of cell nuclei based on their appearance. While the analysis of cytological samples has been automated successfully for a long time, the complexity of histological tissue samples has prevented a reliable classification with machine vision techniques. We approach this complex problem in multiple stages, analyzing first image quality, staining quality, and tissue appearance, before segmenting nuclei and finally classifying or grading areas of tissue. The key step is the training of a classifier to judge the nuclei segmentation quality. Using active learning techniques, we train this classifier to identify problems in the image as well as weaknesses of the image analysis tools. This way we obtain robust nuclear segmentation allowing precise measurements of features that can be used safely for classification. We validate our findings on several hundred cases of breast cancer, demonstrating that automatic pleomorphism grading is possible with high accuracy. This technique can provide a stable and objective basis for what has been a subjective process that suffers from low reproducibility.
Keywords :
biological tissues; cancer; cellular biophysics; computer vision; image classification; image sampling; image segmentation; learning (artificial intelligence); medical image processing; microscopy; nucleus; active learning; cancer diagnostics; cell nuclear pleomorphism grading classification; cytological sample analysis; histological micrograph; image quality; machine vision; nuclei segmentation quality; staining quality; tissue appearance; Breast cancer; Image analysis; Image color analysis; Image edge detection; Image segmentation; Nuclear measurements; Reproducibility of results; Robustness; Shape; Transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
ISSN :
1051-4651
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
Type :
conf
DOI :
10.1109/ICPR.2008.4761112
Filename :
4761112
Link To Document :
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