Image mining for investigative pathology using optimized feature extraction and data fusion

Wenjin Chen, Peter Meer, Bogdan Georgescu, Wei He, Lauri A. Goodell, David J. Foran

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

In many subspecialties of pathology, the intrinsic complexity of rendering accurate diagnostic decisions is compounded by a lack of definitive criteria for detecting and characterizing diseases and their corresponding histological features. In some cases, there exists a striking disparity between the diagnoses rendered by recognized authorities and those provided by non-experts. We previously reported the development of an Image Guided Decision Support (IGDS) system, which was shown to reliably discriminate among malignant lymphomas and leukemia that are sometimes confused with one another during routine microscopic evaluation. As an extension of those efforts, we report here a web-based intelligent archiving subsystem that can automatically detect, image, and index new cells into distributed ground-truth databases. Systematic experiments showed that through the use of robust texture descriptors and density estimation based fusion the reliability and performance of the governing classifications of the system were improved significantly while simultaneously reducing the dimensionality of the feature space.

Original languageEnglish (US)
Pages (from-to)59-72
Number of pages14
JournalComputer Methods and Programs in Biomedicine
Volume79
Issue number1
DOIs
StatePublished - Jul 2005

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Health Informatics

Keywords

  • Automated digital microscopy
  • Content-based image retrieval
  • Data fusion
  • Texture analysis
  • Unsupervised cell imaging

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