Pattern Recognition
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised cell nucleus segmentation with active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Computer and Robot Vision
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
WaterBalloons: A hybrid watershed Balloon Snake segmentation
Image and Vision Computing
Nucleus and cytoplast contour detector of cervical smear image
Pattern Recognition Letters
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Effective segmentation and classification for HCC biopsy images
Pattern Recognition
Computerized cell image analysis: past, present, and future
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Performance measures for object detection evaluation
Pattern Recognition Letters
An Automated Method for Segmentation of Epithelial Cervical Cells in Images of ThinPrep
Journal of Medical Systems
Segmentation of Cervical Cell Images
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images
Pattern Recognition Letters
IEEE Transactions on Information Technology in Biomedicine
Edge Enhancement Nucleus and Cytoplast Contour Detector of Cervical Smear Images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiscale gradient watersheds of color images
IEEE Transactions on Image Processing
Debris removal in Pap-smear images
Computer Methods and Programs in Biomedicine
Automatic cervical cell segmentation and classification in Pap smears
Computer Methods and Programs in Biomedicine
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The Pap smear test is a manual screening procedure that is used to detect precancerous changes in cervical cells based on color and shape properties of their nuclei and cytoplasms. Automating this procedure is still an open problem due to the complexities of cell structures. In this paper, we propose an unsupervised approach for the segmentation and classification of cervical cells. The segmentation process involves automatic thresholding to separate the cell regions from the background, a multi-scale hierarchical segmentation algorithm to partition these regions based on homogeneity and circularity, and a binary classifier to finalize the separation of nuclei from cytoplasm within the cell regions. Classification is posed as a grouping problem by ranking the cells based on their feature characteristics modeling abnormality degrees. The proposed procedure constructs a tree using hierarchical clustering, and then arranges the cells in a linear order by using an optimal leaf ordering algorithm that maximizes the similarity of adjacent leaves without any requirement for training examples or parameter adjustment. Performance evaluation using two data sets show the effectiveness of the proposed approach in images having inconsistent staining, poor contrast, and overlapping cells.