Automatic cell nuclei segmentation and classification of cervical Pap smear images.

Highlights • The database includes uninvolved cell images. • Multi-feature Mean-shift clustering with flexible mathematical morphology is proposed to segment the cell nuclei. • Different types of features are studied for detecting the dysplastic changes. • Feature selection is used to reduce the time cost and improve the classification performance. Abstract Pathological examination of microscopic image of Pap smear slide remains the main method for cervical cancer diagnosis. The accurate segmentation and classification of images are two important phases of the analysis. Firstly, the Mean-Shift clustering algorithm is applied to obtain regions of interest (ROI) for cell nuclei segmentation. Then the flexible mathematical morphology is applied to split overlapped cell nuclei for better accuracy and robustness. For classification of the images, features based on shape, textural features based on color space and Gabor features are extracted and put together to obtain better classification performance. The optimal feature set is obtained by chain-like agent genetic algorithm (CAGA), P-value and maximum relevance-minimum multicollinearity (MRmMC). The proposed segmentation and classification methods were tested on 362 cervical Pap smear images. Experimental results showed that the cervical cell nuclei can be segmented by the proposed segmentation method with high effective segmentation results (Sensitivity: 94.25%±1.03% and Specificity 93.45%±1.14%). The feature selection method based on CAGA with Gabor features has the highest classification performance for normal, uninvolved and abnormal images (more than 96% accuracy). The proposed method can automatically and effectively segment cell nuclei of microscopic images. From the experimental results, Gabor features and feature selection based on CAGA are apparently helpful for improving the performance of classification. [ABSTRACT FROM AUTHOR]