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%.