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An image analysis‐based approach for automated counting of cancer cell nuclei in tissue sections
Author(s) -
Loukas Constantinos G.,
Wilson George D.,
Vojnovic Borivoj,
Linney Alf
Publication year - 2003
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.10060
Subject(s) - hemocytometer , cell counting , thresholding , magnification , artificial intelligence , computer science , stain , histogram , cytometry , image processing , pathology , staining , pattern recognition (psychology) , biomedical engineering , cell , biology , flow cytometry , cell cycle , medicine , image (mathematics) , microbiology and biotechnology , genetics
Background Semiquantitative evaluation and manual cell counting are the commonly used procedures to assess positive staining of molecular markers in tissue sections. Manual counting is also a laborious task in which consistent objectivity is difficult to achieve. Recently, image analysis has been explored, but the studies reported were limited to histological images acquired at high magnification and containing uniformly stained cells. Methods The analyzed material consisted of histological sections from different squamous cell cancers that had stained for proliferation using Ki‐67 and cyclin A detection. The first step of the method was based on detecting the overall number of cells irrespective to their stain, using second‐order edge detection methodology. Then proliferating cells were located using principal component analysis (PCA) of the color image, combined with histogram thresholding. Results The algorithms' performances were validated on tissue section images encountered in routine clinical practice by comparison with objective measures of performance and manual cell identification. The algorithms correlated closely with manual counting of all cells (r 2 = 0.96–0.97) and stained cells (4–7% cell count error). Conclusions Cell counting in complex large‐scale histological images could be applied in routine practice using edge and color information. The proposed technique provides several benefits, such as speed of analysis, consistency, and automation. Moreover, it is faster than human observation and could replace the laborious task of manual cell counting. Cytometry Part A 55A:30–42, 2003. © 2003 Wiley‐Liss, Inc.