Assessing estrogen receptors' status by texture analysis of breast tissue specimens and pattern recognition methods

  • Authors:

  • Spiros Kostopoulos;Dionisis Cavouras;Antonis Daskalakis;Ioannis Kalatzis;Panagiotis Bougioukos;George Kagadis;Panagiota Ravazoula;George Nikiforidis

  • Affiliations:

  • Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio, Greece;Medical Image and Signal Processing Laboratory, Department of Medical Instruments Technology, Technological Educational Institute of Athens, Athens, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio, Greece;Medical Image and Signal Processing Laboratory, Department of Medical Instruments Technology, Technological Educational Institute of Athens, Athens, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio, Greece;Department of Pathology, University Hospital of Patras, Rio, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio, Greece

  • Venue:
  • CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
  • Year:
  • 2007

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Abstract

An image analysis system (IAS) was developed for the quantitative assessment of estrogen receptor's (ER) positive status from breast tissue microscopy images. Twenty-four cases of breast cancer biopsies, immunohisto-chemically (IHC) stained for ER, were microscopically assessed by a histopathologist, following a clinical routine scoring protocol. Digitized microscopy views of the specimens were used in the IAS's design. IAS comprised a/image segmentation, for nuclei determination, b/extraction of textural features, by processing of nuclei-images utilizing the Laws and Gabor filters and by calculating textural features from the processed nuclei-images, and c/PNN and SVM classifiers design, for discriminating positively stained nuclei. The proportion of the latter in each case's images was compared against the physician's score. Using Spearman's rank correlation, high correlation was found between the histo-pathogist's and IAS's scores (rho=0.89, p