Histology image analysis for carcinoma detection and grading

  • Authors:

  • Lei He;L. Rodney Long;Sameer Antani;George R. Thoma

  • Affiliations:

  • National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA;National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA;National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA;National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2012

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Abstract

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.