Automatic detection of abnormal cells from cervical smear images is extremely demanded in annual diagnosis of women’s cervical cancer. For this medical cell recognition problem, there are three different feature sections, namely cytology morphology, nuclear chromatin pathology and region intensity. The challenges of this problem come from feature combination s and classification accurately and efficiently. Thus, we propose an efficient abnormal cervical cell detection system based on multi-instance extreme learning machine (MI-ELM) to deal with above two questions in one unified framework. MI-ELM is one of the most promising supervised learning classifiers which can deal with several feature sections and realistic classification problems analytically. Experiment results over Herlev dataset demonstrate that the proposed method outperforms three traditional methods for two-class classification in terms of well accuracy and less time.
Overlapping cell segmentation is a prerequisite for the analysis of cervical smear images. Because of the complexity of overlapping situations and the poor contrast of overlapping edges, this problem is one of the most challenges in this field. In this paper, a novel unsupervised segmentation method without needing the training data for overlapping cervical smear images is proposed. First, this method uses a kind of graph cuts to separate all cell clumps from the background. A cell clump may contain the different number of cervical cells. Second, each clump is segmented into non-overlapping regions as rough cells using Voronoi diagram. Third, in order to refine the segmentation of overlapping regions, a minimum enclosing ellipse is used to fit in each rough cell and the overlapped parts of each cell are replaced with the relational regions in this ellipse. Finally, the above overlapped parts and the connected parts of the Voronoi rough cell are merged to form a complete cell. Experiments are conducted on 2 publicly released ISBI datasets and results show that the proposed segmentation method achieves the state-of-art performance.
In an abnormal cervical cell detection system the discriminated abilities of different features are not same so the optimized combination method of all features is an essential component to this system. Feature selection can improve each feature utilization ratio and the performance of the classification problem. The previous efforts of cervical abnormal cell detection are mainly focused on changing feature space into a new one by using a binary weight vector. In this work, the binary weight values are extended to the multiple weight values. According to the statistical distribution situation of the data, an adaptive margin-based weighted feature selection method is proposed in this paper. This method performs best compared with the other 3 methods. The experimental result achieves 96% accuracy in a real-world cervical smear image dataset.