Iterative h‐minima‐based marker‐controlled watershed for cell nucleus segmentation

Automated microscopy imaging systems facilitate high‐throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The marker‐controlled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h‐minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. © 2016 International Society for Advancement of Cytometry

[1]  Mrinal K. Mandal,et al.  An Efficient Technique for Nuclei Segmentation Based on Ellipse Descriptor Analysis and Improved Seed Detection Algorithm , 2014, IEEE Journal of Biomedical and Health Informatics.

[2]  Can Fahrettin Koyuncu,et al.  Smart Markers for Watershed-Based Cell Segmentation , 2012, PloS one.

[3]  John T Elliott,et al.  Comparison of segmentation algorithms for fluorescence microscopy images of cells , 2011, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[4]  Cigdem Demir,et al.  Attributed Relational Graphs for Cell Nucleus Segmentation in Fluorescence Microscopy Images , 2013, IEEE Transactions on Medical Imaging.

[5]  Olli Yli-Harja,et al.  A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search , 2013, Pattern Recognit..

[6]  Lin Yang,et al.  Robust Segmentation of Overlapping Cells in Histopathology Specimens Using Parallel Seed Detection and Repulsive Level Set , 2012, IEEE Transactions on Biomedical Engineering.

[7]  Xiaobo Zhou,et al.  A Novel Cell Segmentation Method and Cell Phase Identification Using Markov Model , 2009, IEEE Transactions on Information Technology in Biomedicine.

[8]  Chanho Jung,et al.  Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification , 2010, IEEE Transactions on Biomedical Engineering.

[9]  Christophoros Nikou,et al.  Overlapping Cell Nuclei Segmentation Using a Spatially Adaptive Active Physical Model , 2012, IEEE Transactions on Image Processing.

[10]  Ioannis Pitas,et al.  Automated evaluation of her-2/neu status in breast tissue from fluorescent in situ hybridization images , 2005, IEEE Transactions on Image Processing.

[11]  Chanho Jung,et al.  Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization , 2010, IEEE Transactions on Biomedical Engineering.

[12]  Tianzi Jiang,et al.  Cell Image Segmentation with Kernel-Based Dynamic Clustering and an Ellipsoidal Cell Shape Model , 2001, J. Biomed. Informatics.

[13]  Qing Yang,et al.  Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events , 2007, IEEE Transactions on Image Processing.

[14]  Salim Arslan,et al.  A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images , 2014, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[15]  Jagath C. Rajapakse,et al.  Segmentation of Clustered Nuclei With Shape Markers and Marking Function , 2009, IEEE Transactions on Biomedical Engineering.

[16]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[17]  Xiaobo Zhou,et al.  Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy , 2006, IEEE Transactions on Biomedical Engineering.

[18]  Sim Heng Ong,et al.  A rule-based approach for robust clump splitting , 2006, Pattern Recognit..

[19]  Xiaobo Zhou,et al.  Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[20]  D Fenistein,et al.  A fast, fully automated cell segmentation algorithm for high‐throughput and high‐content screening , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[21]  C Wählby,et al.  Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections , 2004, Journal of microscopy.

[22]  Mohamed Cheriet,et al.  Automatic segmentation of cells from microscopic imagery using ellipse detection , 2007 .