Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images.

@article{Nandy2012AutomaticSA,
  title={Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images.},
  author={Kaustav Nandy and Prabhakar R. Gudla and Ryan Amundsen and Karen J. Meaburn and Tom Misteli and Stephen J. Lockett},
  journal={Cytometry. Part A : the journal of the International Society for Analytical Cytology},
  year={2012},
  volume={81 9},
  pages={
          743-54
        }
}
Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100-200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large-scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated… CONTINUE READING
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