Conference proceeding
Automated identification of microstructures on histology slides
2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821), v 1, pp 424-427
2004
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Grading of breast cancer and the subsequent treatment options are largely dependent on the pathological examination of the histology slides from the tumor tissue. Tumor grading is currently based on the spatial organization of the tissue, including the distribution of cancer cells, the morphological properties of their nuclei and the presence/absence of cancer-associated surface receptors these cells express. In this study, we have developed an automated image processing method to detect and identify clinically relevant microscopic structures on histology slides. The tissue components identified with our program are as follows: fat cells, stroma, and three morphologically distinct cell nuclei types used in grading cancer on the haematoxylin and eosin (H&E) stained slides. The image processing is based on gray-scale segmentation, feature extraction, supervised learning, subsequent training and clustering. Our automated processing system has an accuracy of 89% /spl plusmn/ 0.8 in correctly identifying the three different nuclei types observed in H & E stained histology slides.
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Details
- Title
- Automated identification of microstructures on histology slides
- Creators
- S Petushi - Drexel UniversityC Katsinis - Drexel UniversityC Coward - Drexel UniversityF Garcia - Drexel UniversityA Tozeren - Drexel UniversityIEEE
- Publication Details
- 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821), v 1, pp 424-427
- Conference
- 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2nd
- Publisher
- IEEE
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science; [Retired Faculty]; Pathology (and Laboratory Medicine)
- Web of Science ID
- WOS:000227671300107
- Scopus ID
- 2-s2.0-17144397665
- Other Identifier
- 991019170487804721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Acoustics
- Imaging Science & Photographic Technology
- Neuroimaging
- Radiology, Nuclear Medicine & Medical Imaging