Physical Sciences and Mathematics Commons^™

Enhancements In Localized Classification For Uterine Cervical Cancer Digital Histology Image Assessment, Peng Guo, Haidar A. Almubarak, Koyel Banerjee, R. Joe Stanley, L. Rodney Long, Sameer K. Antani, George R. Thoma, Rosemary E. Zuna, Shelliane R. Frazier, Randy Hays Moss, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

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 …