Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology

Automated detection and segmentation of nuclear and glandular structures is critical for classification and grading of prostate and breast cancer histopathology. In this paper, we present a methodology for automated detection and segmentation of structures of interest in digitized histopathology images. The scheme integrates image information from across three different scales: (1) lowlevel information based on pixel values, (2) high-level information based on relationships between pixels for object detection, and (3) domain-specific information based on relationships between histological structures. Low-level information is utilized by a Bayesian classifier to generate a likelihood that each pixel belongs to an object of interest. High-level information is extracted in two ways: (i) by a level-set algorithm, where a contour is evolved in the likelihood scenes generated by the Bayesian classifier to identify object boundaries, and (ii) by a template matching algorithm, where shape...

• Type: Conference
• Year: 2008
• Where: ISBI
• Authors: Shivang Naik, Scott Doyle, Shannon Agner, Anant Madabhushi, Michael D. Feldman, John Tomaszewski
• Keywords: Breast Cancer, Breast Cancer Histopathology, ISBI 2008, Medical Imaging, Segmentation Algorithm