Automated segmentation of cell nuclei in fine needle aspirates of the breast.

Objective: To achieve a classifier of breast lesions to discriminate between benign and malignant cases of cytologic smears with automated segmentation image analysis techniques.

Study Design: The techniques were applied to images of epithelial cell nuclei from cytologic smears obtained by fine needle aspiration. The images of the nuclei were taken from 95 cases of malignant lesions and 47 benign (approximately 25 nuclei per case), and 28 nuclear variables were measured. The data were analyzed by a double methodology, discriminant analysis and classification and regression trees (CART), to determine which provided the best results.

Results: CART selected the SD of the nuclear area with correct classification of 85.1% of benign and 94.7% malignant aspirates. Discriminant analysis selected the group of variables formed by axis lengths, SD of the longest axis, sphericity and variance of gray levels, with results similar to those of CART.

Conclusion: Automated segmentation image analysis techniques were effective, and the classifier was quick, simple and efficacious in malignant-benign discrimination.