Unsupervised segmentation and classification of cervical cell images
Pattern Recognition
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Presents algorithms for segmenting three-dimensional (3-D) brightfield microscope images of thick and overlapped regions of Pap smears, acquired using a specially-developed high-speed 3-D microscope system. Algorithms for segmenting these images require a careful tradeoff between sophistication and processing speed, due to extreme image variability and the large volume of data involved. These challenges are overcome by applying a sequence of algorithms including an adaptive clustering algorithm that exploits local contrast and focus features, a boundary extraction and refinement algorithm based on gray-level thinning, a 3-D extension of the watershed algorithm to separate overlapping objects, and a boundary selection algorithm that takes into account a priori known characteristics of nuclei. It has been successfully applied on a variety of images.