A unified model-based image analysis framework for automated detection of precancerous lesions in digitized uterine cervix images

Yeshwanth Srinivasan, Enrique Corona, Brian Nutter, Sunanda Mitra, Sonal Bhattacharya

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

A unified framework for a fully automated diagnostic system for cervical intraepithelial neoplasia (CIN) is proposed. CIN is a detectable and treatable precursor pathology of cancer of the uterine cervix. Algorithms based on mathematical morphology, and clustering based on Gaussian mixture modeling (GMM) in a joint color and geometric feature space, are used to segment macro regions. A non-parametric technique, based on the transformation and analysis of the D(R) (distortion-rate) curve is proposed to assess the model order. This technique provides good starting points to infer the GMM parameters via the expectation-maximization (EM) algorithm, reducing the segmentation time and the chances of getting trapped in local optima. The classification of vascular abnormalities in CIN, such as mosaicism and punctations, is modeled as a texture classification problem, and a solution is attempted by characterizing the neighborhood gray-tone dependences and co-occurrence statistics of the textures. The model presented in this paper provides a sequential framework for translating digital images of the cervix into a complete diagnostic tool, with minimal human intervention. In its current form, the research presented in this work may be used to aid physicians to locate abnormalities due to CIN and assess the best areas for a biopsy.

Original languageEnglish
Pages (from-to)101-111
Number of pages11
JournalIEEE Journal on Selected Topics in Signal Processing
Volume3
Issue number1
DOIs
StatePublished - 2009

Keywords

  • Cervical cancer
  • Cervical intraepithelial neoplasia
  • Expectation-maximization algorithm
  • Gaussian mixture model
  • Rate-distortion theory

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