Literature DB >> 22752135

Overlapping cell nuclei segmentation using a spatially adaptive active physical model.

Marina E Plissiti1, Christophoros Nikou.   

Abstract

A method for the segmentation of overlapping nuclei is presented, which combines local characteristics of the nuclei boundary and a priori knowledge about the expected shape of the nuclei. A deformable model whose behavior is driven by physical principles is trained on images containing a single nuclei, and attributes of the shapes of the nuclei are expressed in terms of modal analysis. Based on the estimated modal distribution and driven by the image characteristics, we develop a framework to detect and describe the unknown nuclei boundaries in images containing two overlapping nuclei. The problem of the estimation of an accurate nucleus boundary in the overlapping areas is successfully addressed with the use of appropriate weight parameters that control the contribution of the image force in the total energy of the deformable model. The proposed method was evaluated using 152 images of conventional Pap smears, each containing two overlapping nuclei. Comparisons with other segmentation methods indicate that our method produces more accurate nuclei boundaries which are closer to the ground truth.

Mesh:

Year:  2012        PMID: 22752135     DOI: 10.1109/TIP.2012.2206041

Source DB:  PubMed          Journal:  IEEE Trans Image Process        ISSN: 1057-7149            Impact factor:   10.856


  11 in total

1.  Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images.

Authors:  Ramin Nateghi; Habibollah Danyali; Mohammad Sadegh Helfroush
Journal:  J Med Syst       Date:  2017-08-14       Impact factor: 4.460

2.  Graph-based segmentation of abnormal nuclei in cervical cytology.

Authors:  Ling Zhang; Hui Kong; Shaoxiong Liu; Tianfu Wang; Siping Chen; Milan Sonka
Journal:  Comput Med Imaging Graph       Date:  2017-01-31       Impact factor: 4.790

3.  An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images.

Authors:  Hwejin Jung; Bilal Lodhi; Jaewoo Kang
Journal:  BMC Biomed Eng       Date:  2019-10-17

Review 4.  Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.

Authors:  Fuyong Xing; Lin Yang
Journal:  IEEE Rev Biomed Eng       Date:  2016-01-06

5.  A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching.

Authors:  Cheng Chen; Wei Wang; John A Ozolek; Gustavo K Rohde
Journal:  Cytometry A       Date:  2013-04-08       Impact factor: 4.355

6.  Computerized delineation of nuclei in liquid-based pap smears stained with immunohistochemical biomarkers.

Authors:  Yi Qin; Ann E Walts; Beatrice S Knudsen; Arkadiusz Gertych
Journal:  Cytometry B Clin Cytom       Date:  2014-10-03       Impact factor: 3.058

7.  Survey statistics of automated segmentations applied to optical imaging of mammalian cells.

Authors:  Peter Bajcsy; Antonio Cardone; Joe Chalfoun; Michael Halter; Derek Juba; Marcin Kociolek; Michael Majurski; Adele Peskin; Carl Simon; Mylene Simon; Antoine Vandecreme; Mary Brady
Journal:  BMC Bioinformatics       Date:  2015-10-15       Impact factor: 3.169

Review 8.  A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification.

Authors:  Teresa Conceição; Cristiana Braga; Luís Rosado; Maria João M Vasconcelos
Journal:  Int J Mol Sci       Date:  2019-10-15       Impact factor: 5.923

9.  Data cluster analysis-based classification of overlapping nuclei in Pap smear samples.

Authors:  Mustafa Guven; Caglar Cengizler
Journal:  Biomed Eng Online       Date:  2014-12-09       Impact factor: 2.819

10.  Large-scale localization of touching somas from 3D images using density-peak clustering.

Authors:  Shenghua Cheng; Tingwei Quan; Xiaomao Liu; Shaoqun Zeng
Journal:  BMC Bioinformatics       Date:  2016-09-15       Impact factor: 3.169

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