Automatic segmentation of granular objects in images: Combining local density clustering and gradient-barrier watershed

Blob or granular object recognition is an image processing task with a rich application background, ranging from cell/nuclei segmentation in biology to nanoparticle recognition in physics. In this study, we establish a new and comprehensive framework for granular object recognition. Local density clustering and connected component analysis constitute the first stage. To separate overlapping objects, we further propose a modified watershed approach called the gradient-barrier watershed, which better incorporates intensity gradient information into the geometrical watershed framework. We also revise the marker-finding procedure to incorporate a clustering step on all the markers initially found, potentially grouping multiple markers within the same object. The gradient-barrier watershed is then conducted based on those markers, and the intensity gradient in the image directly guides the water flow during the flooding process. We also propose an important scheme for edge detection and fore/background separation called the intensity moment approach. Experimental results for a wide variety of objects in different disciplines - including cell/nuclei images, biological colony images, and nanoparticle images - demonstrate the effectiveness of the proposed framework. Propose a general framework for granular object recognition and segmentation.Apply the scheme to a wide variety of applications.Propose a modified watershed approach called gradient-barrier watershed.Incorporate a clustering step in the marker finding procedure.Propose a scheme for edge detection and fore/background separation called intensity moment approach.

[1]  Jian Pei,et al.  DHC: a density-based hierarchical clustering method for time series gene expression data , 2003, Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings..

[2]  Serge Beucher,et al.  Watershed, Hierarchical Segmentation and Waterfall Algorithm , 1994, ISMM.

[3]  Badrinath Roysam,et al.  A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks , 2003, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[4]  Yunmei Chen,et al.  Using Prior Shapes in Geometric Active Contours in a Variational Framework , 2002, International Journal of Computer Vision.

[5]  Sanketh Vittaldas Shetty.  Isotropy criteria and algorithms for data clustering , 2011 .

[6]  Kannappan Palaniappan,et al.  Cell Segmentation Using Coupled Level Sets and Graph-Vertex Coloring , 2006, MICCAI.

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

[8]  Hanan Samet,et al.  Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Guillermo Sapiro,et al.  Simultaneous Object Classification and Segmentation With High-Order Multiple Shape Models , 2010, IEEE Transactions on Image Processing.

[10]  Ana Maria Mendonça,et al.  Evaluation of Symmetry Enhanced Sliding Band Filter for Plant Cell Nuclei Detection in Low Contrast Noisy Fluorescent Images , 2009, ICIAR.

[11]  Peng Zhao,et al.  Supervised learning-based cell image segmentation for P53 immunohistochemistry , 2006, IEEE Transactions on Biomedical Engineering.

[12]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[13]  Badrinath Roysam,et al.  Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nuclei , 2005, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[14]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[15]  Johan H C Reiber,et al.  Detection of pollen grains in multifocal optical microscopy images of air samples , 2009, Microscopy research and technique.

[16]  Brian C. Lovell,et al.  Unsupervised cell nucleus segmentation with active contours , 1998, Signal Process..

[17]  Hanan Samet,et al.  A general approach to connected-component labeling for arbitrary image representations , 1992, JACM.

[18]  Laurent Najman,et al.  Geodesic Saliency of Watershed Contours and Hierarchical Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[20]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[21]  Zainab Abu Bakar,et al.  A Modified Edge-Based Region Growing Segmentation of Geometric Objects , 2011, IVIC.

[22]  Narendra Ahuja,et al.  Location- and Density-Based Hierarchical Clustering Using Similarity Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Serge Beucher,et al.  THE WATERSHED TRANSFORMATION APPLIED TO IMAGE SEGMENTATION , 2009 .

[24]  Bogdan J. Matuszewski,et al.  Multiphase active contour segmentation constrained by evolving medial axes , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[25]  Andrew G. Dempster,et al.  Blood Cell Segmentation Using Minimum Area Watershed and Circle Radon Transformations , 2005, ISMM.

[26]  Nassir Salman,et al.  Image Segmentation Based on Watershed and Edge Detection Techniques , 2006, Int. Arab J. Inf. Technol..

[27]  Eugene W. Myers,et al.  AUTOMATIC SEGMENTATION OF NUCLEI IN 3D MICROSCOPY IMAGES OF C.ELEGANS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[28]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[29]  Kenji Suzuki,et al.  A Run-Based Two-Scan Labeling Algorithm , 2008, IEEE Transactions on Image Processing.

[30]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[31]  J J Vaquero,et al.  Applying watershed algorithms to the segmentation of clustered nuclei. , 1998, Cytometry.

[32]  Daniel P. Huttenlocher,et al.  Distance Transforms of Sampled Functions , 2012, Theory Comput..

[33]  Qing Wang,et al.  Fast and robust segmentation of spherical particles in volumetric data sets from brightfield microscopy , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[34]  Yu Ding,et al.  Segmentation, Inference and Classification of Partially Overlapping Nanoparticles , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Chen Wang,et al.  SUDEPHIC: Self-Tuning Density-Based Partitioning and Hierarchical Clustering , 2004, DASFAA.

[36]  Bidyut Baran Chaudhuri,et al.  An efficient method based on watershed and rule-based merging for segmentation of 3-D histo-pathological images , 2001, Pattern Recognit..

[37]  Huiguang Yang,et al.  Semi-automated segmentation of pollen grains in microscopic images: a tool for three imaging modes , 2013 .

[38]  Patrick Olivier,et al.  Visual Informatics: Sustaining Research and Innovations , 2011, Lecture Notes in Computer Science.

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

[40]  Fabrice Heitz,et al.  Multi-Reference Shape Priors for Active Contours , 2008, International Journal of Computer Vision.

[41]  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.

[42]  Fernand Meyer,et al.  Levelings, Image Simplification Filters for Segmentation , 2004, Journal of Mathematical Imaging and Vision.

[43]  Ana Maria Mendonça,et al.  Cell Nuclei and Cytoplasm Joint Segmentation Using the Sliding Band Filter , 2010, IEEE Transactions on Medical Imaging.