A Fusion-based Approach for Uterine Cervical Cancer Histology Image Classification


Expert pathologists commonly perform visual interpretation of histology slides for cervix tissue abnormality diagnosis. We investigated an automated, localized, fusion-based approach for cervix histology image analysis for squamous epithelium classification into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The epithelium image analysis approach includes medial axis determination, vertical segment partitioning as medial axis orthogonal cuts, individual vertical segment feature extraction and classification, and image-based classification using a voting scheme fusing the vertical segment CIN grades. Results using 61 images showed at least 15.5% CIN exact grade classification improvement using the localized vertical segment fusion versus global image features.


Electrical and Computer Engineering

Keywords and Phrases

Cervical intraepithelial neoplasias; Feature analysis; Feature extraction and classification; Image-based classification; Medial axis determinations; Tissue abnormalities; Uterine cervical cancer; Visual interpretation; Data fusion; Feature extraction; Histology; Image analysis; Image processing; Image segmentation; Image classification; cancer grading; human; pathologist; priority journal; squamous epithelium; uterine cervix cancer; uterine cervix carcinoma in situ; Cervical intraepithelial neoplasia; Algorithms; Female; Humans; Image Enhancement; Image Interpretation; Computer-Assisted; Microscopy; Microtomy; Neoplasm Grading; Neoplasms; Pattern Recognition; Automated; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; Uterine Cervical Neoplasms

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


File Type





© 2013 Elsevier, All rights reserved.

Publication Date

01 Oct 2013