Automatic quantification and classification of cervical cancer via Adaptive Nucleus Shape Modeling
Hady Ahmady Phoulady, Mu Zhou, Dmitry B. Goldgof, Lawrence Hall, Peter R. Mouton · 2016
Decisions about cervical cancer diagnosis and classification currently require microscopic examination of cervical tissue by an expert pathologist. In the present study, which focused on full automation of this approach, we solely use nucleus-level features to classify tissues as normal or cancer. We propose Adaptive Nucleus Shape Modeling (ANSM) algorithm for nucleus-level analysis which consists of two steps to capture the nucleus-level information: adaptive multilevel thresholding segmentation; and shape approximation by ellipse fitting. After applying the proposed algorithm, the features are extracted for tissue classification. Experiments show that ANSM can achieve an accuracy of 93.33% with a false negative rate of zero in classifying cancer and healthy cervical tissues using nucleus texture features. This provides evidence that nucleus-level analysis is valuable in cervical histology image analysis.