Physical Sciences and Mathematics Commons^™

Fuzzy Color Clustering For Melanoma Diagnosis In Dermoscopy Images, Haidar A. Almubarak, R. Joe Stanley, William V. Stoecker, Randy Hays Moss

Electrical and Computer Engineering Faculty Research & Creative Works

A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy clustering ratio over different regions of the lesion over a data set of 517 dermoscopy images consisting of 175 invasive melanomas and 342 benign lesions. Experimental results show that the fuzzy clustering ratio applied over …

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 …