V. B. S. Prasath, F. Bunyak, P. Dale, S. R. Frazier, and K. Palaniappan

IEEE Healthcare Innovation Conference, 2012

histopathology, active contours, classification, features, texture, machine learning, data mining, biomedical

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Abstract

Computer-Aided Diagnosis (CAD) systems for pathologists can act as an intelligent digital assistant supporting automated grading and morphometric-based discovery of tissue features that are important in cancer diagnosis and patient prognosis. Automated image segmentation is an essential com- ponent of computer-based grading in CAD. We describe a novel tissue segmentation algorithm using local feature-based active contours in a globally convex formulation. Preliminary results using the Stanford Tissue MicroArray database shows promising stromal/epithelial superpixel segmentation.

Reference

@inproceedings{Surya:HIC-2012-histopath,
  author = "V. B. S. Prasath and F. Bunyak and P. Dale and S. R. Frazier and K. Palaniappan",
  title = "Segmentation of breast cancer tissue microarrays for computer-aided diagnosis in pathology",
  year = 2012,
  booktitle = "IEEE Healthcare Innovation Conference",
  keywords = "histopathology, active contours, classification, features, texture, machine learning, data mining, biomedical"
}

Plain Text

V. B. S. Prasath, F. Bunyak, P. Dale, S. R. Frazier, and K. Palaniappan. Segmentation of breast cancer tissue microarrays for computer-aided diagnosis in pathology. IEEE Healthcare Innovation Conference, 2012.

#144: Segmentation of breast cancer tissue microarrays for computer-aided diagnosis in pathology

histopathology, active contours, classification, features, texture, machine learning, data mining, biomedical

Abstract

Reference

Plain Text