Clustering nuclei using machine learning techniques
Cervical cancer is the second most common cancer among women. Meanwhile, cervical cancer could be largely preventable and curable with regular Pap tests. Nuclei changes in the cervix could be found by this test. Accurate nuclei detection is extremely critical as it is the previous step of analysing nuclei changes and diagnosis afterwards. Recently, computer-aided 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. Second, we chose some high-relevant features from all the features we obtained in last step via F-score, which is based on to what extent one feature attributes to results. 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. With the 9 selected features from the original 13 features, we came by the promising classification accuracy (97.8%).