The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images
Abstract
We present a study of image features for cancer diagnosis and Gleason grading of the histological images of prostate. In diagnosis, the tissue image is classified into the tumor and nontumor classes. In Gleason grading, which characterizes tumor aggressiveness, the image is classified as containing a low- or high-grade tumor. The image sets used in this paper consisted of 367 and 268 color images for the diagnosis and Gleason grading problems, respectively, and were captured from representative areas of hematoxylin and eosin-stained tissue retrieved from tissue microarray cores or whole sections. The primary contribution of this paper is to aggregate color, texture, and morphometric cues at the global and histological object levels for classification. Features representing different visual cues were combined in a supervised learning framework. We compared the performance of Gaussian, k-nearest neighbor, and support vector machine classifiers together with the sequential forward feature selection algorithm. On diagnosis, using a five-fold cross-validation estimate, an accuracy of 96.7% was obtained. On Gleason grading, the achieved accuracy of classification into low- and high-grade classes was 81.0%.
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LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
(2012) · Encyclopedia of the Sciences of Learning · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,370
Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
Daubechies, I., & Heil, C. (1992) · Computers in Physics · doi:10.1063/1.4823127 · Cited by 15,631
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,031
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,122
Introduction to statistical pattern recognition (2nd ed.)
Fukunaga, K. (1990) · Academic Press Professional, Inc. eBooks · Cited by 3,797
Pattern Classification (2nd ed.)
Duda, R. O., Hart, P. E., & Stork, D. G. (1999) · Cited by 2,520
Barker, L. (2002) · Technometrics · doi:10.1198/tech.2002.s659 · Cited by 2,477
Cited by
Histopathological Image Analysis: A Review
Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034865 · Cited by 2,052
DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
Chen, H., Qi, X., Yu, L., & Heng, P.-A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.273 · Cited by 623
A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association
Zarella, M. D., Bowman;, D., Aeffner, F., Farahani, N., Xthona;, A., Absar, S. F., Parwani, A., Bui, M., & Hartman, D. J. (2019) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0343-ra · Cited by 404
Constrained deep weak supervision for histopathology image segmentation
Jia, Z., Huang, X., Chang, E. I.-C., & Xu, Y. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2724070 · Cited by 242
High-throughput histopathological image analysis via robust cell segmentation and hashing
Zhang, X., Xing, F., Su, H., Yang, L., & Zhang, S. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.10.005 · Cited by 87
Metadata
- Authors
- Tabesh, A., Teverovskiy, M., Pang, H.-Y., Kumar, V. P., Verbel, D., Kotsianti, A., & Saidi, O.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2007-10
- Volume
- 26
- Issue
- 10
- Pages
- 1366-1378
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2156398782
- Cited by
- 351
- References
- 20
- FWCI
- 12.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tabesh, A., Teverovskiy, M., Pang, H.-Y., Kumar, V. P., Verbel, D., Kotsianti, A., & Saidi, O. (2007). Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2007.898536
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