The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Histology image analysis for carcinoma detection and grading
Open accessAbstract
This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.
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Citations
Cites
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BackgroundFast approximate energy minimization via graph cuts
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Snakes, shapes, and gradient vector flow
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Gene-expression profiles predict survival of patients with lung adenocarcinoma
Beer, D. G., Kardia, S. L. R., Huang, C.-C., Giordano, T. J., Levin, A. M., Misek, D. E., Lin, L., Chen, G., Gharib, T. G., Thomas, D. G., et al. (2002) · Nature Medicine · doi:10.1038/nm733 · Cited by 2,025
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Cited by
Cruz-Roa, A. A., Arevalo Ovalle, J. E., Madabhushi, A., & González Osorio, F. A. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_50 · Cited by 403
Teramoto, A., Tsukamoto, T., Kiriyama, Y., & Fujita, H. (2017) · BioMed Research International · doi:10.1155/2017/4067832 · Cited by 279
Noorbakhsh, J., Farahmand, S., Foroughi pour, A., Namburi, S., Caruana, D., Rimm, D., Soltanieh-ha, M., Zarringhalam, K., & Chuang, J. H. (2020) · Nature Communications · doi:10.1038/s41467-020-20030-5 · Cited by 206
Pancreatic cancer subtypes: a roadmap for precision medicine
Torres, C., & Grippo, P. J. (2018) · Annals of Medicine · doi:10.1080/07853890.2018.1453168 · Cited by 104
Sailem, H. Z., Rittscher, J., & Pelkmans, L. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199083 · Cited by 16
Metadata
- Authors
- He, L., Long, L. R., Antani, S., & Thoma, G. R.
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2012-09
- Volume
- 107
- Issue
- 3
- Pages
- 538-556
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W1965451568
- Cited by
- 389
- References
- 8
- FWCI
- 16.46
- Type
- journal article
- Language
- en
- Open access
- green
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