Nuclei-based Features for Uterine Cervical Cancer Histology Image Analysis with Fusion-based Classification
Cervical cancer, which has been affecting women worldwide as the second most common cancer, can be cured if detected early and treated well. Routinely, expert pathologists visually examine histology slides for cervix tissue abnormality assessment. In previous research, we investigated an automated, localized, fusion-based approach for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 61 digitized histology images. This research introduces novel acellular and atypical cell concentration features computed from vertical segment partitions of the epithelium region within digitized histology images to quantize the relative increase in nuclei numbers as the CIN grade increases. Based on CIN grade assessments from two expert pathologists, image-based epithelium classification is investigated with voting fusion of vertical segments using support vector machine (SVM) and Linear Discriminant Analysis (LDA) approaches. Leave-one-out is used for training and testing for CIN classification, achieving an exact grade labeling accuracy as high as 88.5%.
P. Guo 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, "Nuclei-based Features for Uterine Cervical Cancer Histology Image Analysis with Fusion-based Classification," IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 6, pp. 1595-1607, Institute of Electrical and Electronics Engineers Inc., Nov 2015.
The definitive version is available at https://doi.org/10.1109/JBHI.2015.2483318
Electrical and Computer Engineering
Keywords and Phrases
Discriminant analysis; Diseases; Histology; Image analysis; Image fusion; Image processing; Image retrieval; Image segmentation; Support vector machines; Cell concentrations; Cervical cancers; Cervical intraepithelial neoplasias; Labeling accuracies; Linear discriminant analysis; Tissue abnormalities; Training and testing; Uterine cervical cancer; Image classification
International Standard Serial Number (ISSN)
Article - Journal
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