Object segmentation within microscope images of palynofacies
Computers & Geosciences
Level Set Segmentation of Cellular Images Based on Topological Dependence
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Morphological multiscale decomposition of connected regions with emphasis on cell clusters
Computer Vision and Image Understanding
On the decomposition of cell clusters
Journal of Mathematical Imaging and Vision
Segmentation of Neural Stem/Progenitor Cells Nuclei within 3-D Neurospheres
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Classifier ensemble for an effective cytological image analysis
Pattern Recognition Letters
Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images
Computers in Biology and Medicine
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We present a segmentation method that works for overlapping and closely packed nuclei in noisy images that have high variation in background intensity. The method has been tested on .uorescence in-situ hybridisation images of interphase leucocyte nuclei. Accurate segmentation is required in support of an automatic procedure for assaying telomere content on a per area per nucleus basis. The method first finds a single seed point for each nucleus that uniquely identifies that nucleus. Seed points are located by an efficient iterative mode-finding algorithm based on robust nonparametric density estimation. Acting simultaneously on all nuclei in the image, and using the seed points as origins, flexible closed contours are dilated until each nucleus is circumscribed. Unlike previous approaches, thecontour equations include a repulsive term that prevents different contours from intersecting, thereby preserving the identity of nearby or overlapping nuclei, and the contour is adaptively remeshed for greater efficiency. The locations of the seed points are not critical in providing an accurate segmentation. The advantages of this method from an implementation point of view are that the computation of seed points and contours is highly efficient and robust compared with alternative approaches. The method is illustrated using data from a clinical pilot study.