Embedding topic discovery in conditional random fields model for segmenting nuclei using multispectral data

Xuqing Wu, Mojgan Amrikachi, Shishir K. Shah

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations


Segmentation of cells/nuclei is a challenging problem in 2-D histological and cytological images. Although a large number of algorithms have been proposed, newer efforts continue to be devoted to investigate robust models that could have high level of adaptability with regard to considerable amount of image variability. In this paper, we propose a multiclassification conditional random fields (CRFs) model using a combination of low-level cues (bottom-up) and high-level contextual information (top-down) for separating nuclei from the background. In our approach, the contextual information is extracted by an unsupervised topic discovery process, which efficiently helps to suppress segmentation errors caused by intensity inhomogeneity and variable chromatin texture. In addition, we propose a multilayer CRF, an extension of the traditional single-layer CRF, to handle high-dimensional dataset obtained through spectral microscopy, which provides combined benefits of spectroscopy and imaging microscopy, resulting in the ability to acquire spectral images of microscopic specimen. The approach is evaluated with color images, as well as spectral images. The overall accuracy of the proposed segmentation algorithm reaches 95 when applying multilayer CRF model to the spectral microscopy dataset. Experiments also show that our method outperforms seeded watershed, a widely used algorithm for cell segmentation.

Original languageEnglish (US)
Article number2188892
Pages (from-to)1539-1549
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number6
StatePublished - 2012


  • Conditional random fields (CRFs)
  • probabilistic latent semantic analysis (pLSA)
  • segmentation
  • spectral microscopy
  • topic discovery

ASJC Scopus subject areas

  • Biomedical Engineering


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