LAUSR

Objective: A novel fuzzy reasoning model using temporal grayscale change and texture information of acetic acid test cervical images was developed to classify the patients at risk for Cervical Intraepithelial Neoplasia (CIN). Methods: Pre- and post-acetic acid test images were obtained from 505 patients (383 CIN negative and 122 CIN positive). An automatic image segmentation algorithm was implemented to extract the acetowhite region, followed by feature extraction of the: 1) temporal grayscale change from pre- to post-images; 2) texture coarseness; and 3) texture complexity of post-test images. The index of CIN was derived based on fuzzy reasoning incorporating expert knowledge. Logistic regression was built with cross validation to compare the classification performance between different combinations of features. Results: The averaged sensitivity was 80.8%, 80.9%, and 82.8% and specificity was 82.0%, 87.4%, and 86.2% for three individual features, respectively. The combination of all three features significantly improved the sensitivity (84.7%) without a significant loss of specificity (86.5%). The fuzzy reasoning model further improved the sensitivity significantly (sensitivity 85.9% and specificity 86.6%). Conclusion: Image texture features outperformed temporal grayscale change in specificity. Combining grayscale- and texture-based features could improve the overall classification performance, creating a synergistic effect. The proposed fuzzy reasoning model could further increase the sensitivity without requiring additional features. Significance: The proposed automatic CIN classification algorithm could provide a useful screening tool in the prevention of cervical cancers.