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http://yadda.icm.edu.pl:443/baztech/element/bwmeta1.element.baztech-3bf14d41-2b9a-4024-bd8b-82765275456b

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

A novel approach for detection and delineation of cell nuclei using feature similarity index measure

Autorzy John, J.  Nair, M. S.  Kumar, P. R. A.  Wilscy, M. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Accurate image segmentation of cells and tissues is a challenging research area due to its vast applications in medical diagnosis. Seed detection is the basic and most essential step for the automated segmentation of microscopic images. This paper presents a robust, accurate and novel method for detecting cell nuclei which can be efficiently used for cell segmentation. We propose a template matching method using a feature similarity index measure (FSIM) for detecting nuclei positions in the image which can be further used as seeds for segmentation tasks. Initially, a Fuzzy C-Means clustering algorithm is applied on the image for separating the foreground region containing the individual and clustered nuclei regions. FSIM based template matching approach is then used for nuclei detection. FSIM makes use of low level texture features for comparisons and hence gives good results. The performance of the proposed method is evaluated on the gold standard dataset containing 36 images (_8000 nuclei) of tissue samples and also in vitro cultured cell images of Stromal Fibroblasts (5 images) and Human Macrophage cell line (4 images) using the statistical measures of Precision and Recall. The results are analyzed and compared with other state-of-the-art methods in the literature and software tools to prove its efficiency. Precision is found to be comparable and the Recall rate is found to exceed 92% for the gold standard dataset which shows considerable performance improvement over existing methods.
Słowa kluczowe
PL obraz mikroskopowy   wskaźnik podobieństwa   makrofagi   jądro komórkowe   segmentacja komórki  
EN microscopic image   feature similarity index measure   macrophages   cell nuclei   seed detection   cell segmentation  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2016
Tom Vol. 36, no. 1
Strony 76--88
Opis fizyczny Bibliogr. 37 poz., rys., tab.
Twórcy
autor John, J.
  • Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India, jisha.json@gmail.com
autor Nair, M. S.
autor Kumar, P. R. A.
autor Wilscy, M.
  • Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India, wilscy.m@gmail.com
Bibliografia
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Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-3bf14d41-2b9a-4024-bd8b-82765275456b
Identyfikatory
DOI 10.1016/j.bbe.2015.11.002