Abstract
Background: In previous research, we introduced an automated, localized, fusion-based approach for classifying uterine cervix squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on digitized histology image analysis. As part of the CIN assessment process, acellular and atypical cell concentration features were computed from vertical segment partitions of the epithelium region to quantize the relative distribution of nuclei.
Methods: Feature data was extracted from 610 individual segments from 61 images for epithelium classification into categories of Normal, CIN1, CIN2, and CIN3. The classification results were compared against CIN labels obtained from two pathologists who visually assessed abnormality in the digitized histology images. In this study, individual vertical segment CIN classification accuracy improvement is reported using the logistic regression classifier for an expanded data set of 118 histology images.
Results: We analyzed the effects on classification using the same pathologist labels for training and testing versus using one pathologist labels for training and the other for testing. Based on a leave-one-out approach for classifier training and testing, exact grade CIN accuracies of 81.29% and 88.98% were achieved for individual vertical segment and epithelium whole-image classification, respectively.
Conclusions: The Logistic and Random Tree classifiers outperformed the benchmark SVM and LDA classifiers from previous research. The Logistic Regression classifier yielded an improvement of 10.17% in CIN Exact grade classification results based on CIN labels for training-testing for the individual vertical segments and the whole image from the same single expert over the baseline approach using the reduced features. Overall, the CIN classification rates tended to be higher using the training-testing labels for the same expert than for training labels from one expert and testing labels from the other expert. The Exact class fusion- based CIN discrimination results obtained in this study are similar to the Exact class expert agreement rate.
Recommended Citation
P. Guo and H. A. Almubarak and K. Banerjee and R. J. Stanley and L. R. Long and S. K. Antani and G. R. Thoma and R. E. Zuna and S. R. Frazier and R. H. Moss and W. V. Stoecker, "Enhancements in Localized Classification for Uterine Cervical Cancer Digital Histology Image Assessment," Journal of Pathology Informatics, vol. 7, no. 51, Medknow Publications, Dec 2016.
The definitive version is available at https://doi.org/10.4103/2153-3539.197193
Department(s)
Electrical and Computer Engineering
Second Department
Chemistry
Sponsor(s)
National Institutes of Health (U.S.). Intramural Research Program
National Library of Medicine (U.S.)
Lister Hill National Center for Biomedical Communications
Keywords and Phrases
Cervical Cancer; Cervical Intraepithelial Neoplasia; Fusion-Based Classification; Image Processing
International Standard Serial Number (ISSN)
2229-5089; 2153-3539
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2016 Medknow Publications, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.
Publication Date
01 Dec 2016
PubMed ID
28163974
Comments
This research was supported (in part) by the Intramural Research Program of the National Institutes of Health, National Library of Medicine, and Lister Hill National Center for Biomedical Communications.