Detection of nuclei clusters from cervical cancer microscopic imagery using C4.5
Yu Peng, Mi‐Ra Park, Min Gang Xu, Suhuai Luo, Jesse Sheng Jin, Yue Cui, Wai‐Man Wong · 2010
Cervical cancer is the second most common cancer among women. At the same time, cervical cancer could be largely preventable and curable with regular Pap tests. This test can find nuclei changes in the cervix. Accurate nuclei detection is extremely critical as it is the previous step of analysing nuclei changes and diagnosis afterwards. In recent years, automatic nuclei segmentation has increased dramatically. Although such algorithms could be utilised in the situation for sparse nuclei since they are intuitively detected, the segmentation for the complicated nuclei clusters is still challenging task. This paper presents a new methodology for the detection of cervical nuclei clusters. We first detect all the nuclei from the cervical microscopic image by an ellipse fitting algorithm. All the ellipses are then classified into single ones and cluster ones by C4.5 decision tree with selected features. We evaluated the performance of this method by the classification accuracy, sensitivity, and cluster predictive value. The result shown that the promising classification accuracy (97.8%) is obtained using C4.5 with 9 relative features.