NDLI: Embedding Topic Discovery in Conditional Random Fields Model for Segmenting Nuclei Using Multispectral Data

Content Provider	IEEE Xplore Digital Library
Author	Xuqing Wu Amrikachi, M. Shah, S.K.
Copyright Year	1964
Abstract	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.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	11
File Size	949140
Starting Page	1539
Ending Page	1549
File Format	PDF
ISSN	00189294
Volume Number	59
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image color analysis Image edge detection Microscopy Color Adaptation models Data models topic discovery Conditional random fields (CRFs) probabilistic latent semantic analysis (pLSA) segmentation spectral micro-scopy
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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