Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering.
In this paper, we present a fully automated method for cell nuclei detection in Pap smear images. The locations of the candidate nuclei centroids in the image are detected with morphological analysis and they are refined in a second step, which incorporates a priori knowledge about the circumference of each nucleus. The elimination of the undesirable artifacts is achieved in two steps: the application of a distance-dependent rule on the resulted centroids; and the application of classification algorithms. In our method, we have examined the performance of an unsupervised (fuzzy C-means) and a supervised (support vector machines) classification technique. In both classification techniques, the effect of the refinement step improves the performance of the clustering algorithm. The proposed method was evaluated using 38 cytological images of conventional Pap smears containing 5617 recognized squamous epithelial cells. The results are very promising, even in the case of images with high degree of cell overlapping.
Plissiti
Marina E
ME
Department of Computer Science, University of Ioannina, Ioannina, Greece. marina@cs.uoi.gr
Nikou
Christophoros
C
Charchanti
Antonia
A
D000465:Algorithms; D002467:Cell Nucleus / Q000648:ultrastructure*; D002584:Cervix Uteri / Q000166:cytology / Q000648:ultrastructure*; D016000:Cluster Analysis; D019295:Computational Biology; D004847:Epithelial Cells / Q000166:cytology / Q000648:ultrastructure; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D065006:Papanicolaou Test; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D014626:Vaginal Smears