Automated classification of breast pathology using local measures of broadband reflectance. Academic Article uri icon

Overview

abstract

  • We demonstrate that morphological features pertinent to a tissue's pathology may be ascertained from localized measures of broadband reflectance, with a mesoscopic resolution (100-μm lateral spot size) that permits scanning of an entire margin for residual disease. The technical aspects and optimization of a k-nearest neighbor classifier for automated diagnosis of pathologies are presented, and its efficacy is validated in 29 breast tissue specimens. When discriminating between benign and malignant pathologies, a sensitivity and specificity of 91 and 77% was achieved. Furthermore, detailed subtissue-type analysis was performed to consider how diverse pathologies influence scattering response and overall classification efficacy. The increased sensitivity of this technique may render it useful to guide the surgeon or pathologist where to sample pathology for microscopic assessment.

authors

  • Laughney, Ashley
  • Krishnaswamy, Venkataramanan
  • Garcia-Allende, Pilar Beatriz
  • Conde, Olga M
  • Wells, Wendy A
  • Paulsen, Keith D
  • Pogue, Brian W

publication date

  • January 1, 2010

Research

keywords

  • Algorithms
  • Breast Neoplasms
  • Diagnosis, Computer-Assisted
  • Pattern Recognition, Automated
  • Photometry

Identity

PubMed Central ID

  • PMC3032233

Scopus Document Identifier

  • 79953107219

Digital Object Identifier (DOI)

  • 10.1117/1.3155512

PubMed ID

  • 21198193

Additional Document Info

volume

  • 15

issue

  • 6