Interactive segmentation based on component-trees

Component-trees associate to a discrete grey-level image a descriptive data structure induced by the inclusion relation between the binary components obtained at successive level-sets. This article presents an original interactive segmentation methodology based on component-trees. It consists of the extraction of a subset of the image component-tree, enabling the generation of a binary object which fits at best (with respect to the grey-level structure of the image) a given binary target selected beforehand in the image. A proof of the algorithmic efficiency of this methodological scheme is proposed. Concrete application examples on magnetic resonance imaging (MRI) data emphasise its actual computational efficiency and its usefulness for interactive segmentation of real images.

[1]  William A. Barrett,et al.  Interactive live-wire boundary extraction , 1997, Medical Image Anal..

[2]  Michael H. F. Wilkinson,et al.  Volumetric Attribute Filtering and Interactive Visualization Using the Max-Tree Representation , 2007, IEEE Transactions on Image Processing.

[3]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid , 2012 .

[4]  Michael H. F. Wilkinson,et al.  Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Michael H. F. Wilkinson,et al.  Vector-Attribute Filters , 2005, ISMM.

[6]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  A J Barkovich,et al.  Magnetic resonance imaging of the fetal brain and spine: an increasingly important tool in prenatal diagnosis, part 1. , 2006, AJNR. American journal of neuroradiology.

[8]  Michael W. Berry,et al.  Using dendronal signatures for feature extraction and retrieval , 2000, Int. J. Imaging Syst. Technol..

[9]  Ronald Jones,et al.  Attribute Openings, Thinnings, and Granulometries , 1996, Comput. Vis. Image Underst..

[10]  Andrew Mehnert,et al.  An improved seeded region growing algorithm , 1997, Pattern Recognit. Lett..

[11]  Nicolas Passat,et al.  Attribute-Filtering and Knowledge Extraction for Vessel Segmentation , 2010, ISVC.

[12]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Pascal Monasse,et al.  Scale-Space from a Level Lines Tree , 2000, J. Vis. Commun. Image Represent..

[14]  J. Hartigan.  Statistical theory in clustering , 1985 .

[15]  Jean Paul Frédéric Serra.  Connectivity on Complete Lattices , 2004, Journal of Mathematical Imaging and Vision.

[16]  D. R. Fulkerson,et al.  Flows in Networks. , 1964 .

[17]  Arnaldo de Albuquerque Araújo,et al.  1D Component tree in linear time and space and its application to gray-level image multithresholding , 2007, ISMM.

[18]  Laurent Wendling,et al.  A document binarization method based on connected operators , 2010, Pattern Recognit. Lett..

[19]  Ronald Jones,et al.  Connected Filtering and Segmentation Using Component Trees , 1999, Comput. Vis. Image Underst..

[20]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[22]  Noel E. O'Connor,et al.  A comparative evaluation of interactive segmentation algorithms , 2010, Pattern Recognit..

[23]  Hui Gao,et al.  Concurrent Computation of Attribute Filters on Shared Memory Parallel Machines , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[25]  Emmanuel Bertin,et al.  Effective Component Tree Computation with Application to Pattern Recognition in Astronomical Imaging , 2007, 2007 IEEE International Conference on Image Processing.

[26]  Michael H. F. Wilkinson,et al.  Mask-Based Second-Generation Connectivity and Attribute Filters , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[28]  Michel Couprie,et al.  Building the Component Tree in Quasi-Linear Time , 2006, IEEE Transactions on Image Processing.

[29]  Tomasz Adamek,et al.  Using contour information and segmentation for object registration, modeling and retrieval , 2006 .

[30]  Nicolas Passat,et al.  Segmentation of Complex Images Based on Component-Trees: Methodological Tools , 2009, ISMM.

[31]  Michael W. L. Chee,et al.  Skull stripping using graph cuts , 2010, NeuroImage.

[32]  Belma Dogdas,et al.  Segmentation of skull and scalp in 3‐D human MRI using mathematical morphology , 2005, Human brain mapping.

[33]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[34]  Jacques Demongeot,et al.  Tree Representation for Image Matching and Object Recognition , 1999, DGCI.

[35]  Hugues Talbot,et al.  Mathematical Morphology: from theory to applications , 2013 .

[36]  Volodymyr Mosorov.  A main stem concept for image matching , 2005, Pattern Recognit. Lett..

[37]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[38]  Isabelle Bloch,et al.  Fast fuzzy connected filter implementation using max-tree updates , 2010, Fuzzy Sets Syst..

[39]  Philippe Salembier,et al.  Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval , 2000, IEEE Trans. Image Process..

[40]  Philippe Salembier,et al.  Antiextensive connected operators for image and sequence processing , 1998, IEEE Trans. Image Process..

[41]  Michael H. F. Wilkinson,et al.  Shape Preserving Filament Enhancement Filtering , 2001, MICCAI.

[42]  Laurent Wendling,et al.  Combining Shape Descriptors and Component-tree for Recognition of Ancient Graphical Drop Caps , 2009, VISAPP.

[43]  Isabelle Bloch,et al.  opologically controlled segmentation of 3D magnetic resonance images of the head by using morphological operators , 2003, Pattern Recognit..

[44]  Jacques Demongeot,et al.  Efficient Algorithms to Implement the Confinement Tree , 2000, DGCI.

[45]  Nicolas Passat,et al.  Segmentation using vector-attribute filters: methodology and application to dermatological imaging , 2007, ISMM.

[46]  Nicolas Passat,et al.  An extension of component-trees to partial orders , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[47]  William A. Barrett,et al.  Interactive Segmentation with Intelligent Scissors , 1998, Graph. Model. Image Process..

[48]  E. R. Urbach,et al.  Shape-only granulometries and gray-scale shape filters , 2002 .

[49]  Nicolas Passat,et al.  Component-Trees and Multi-value Images: A Comparative Study , 2009, ISMM.

[50]  Naif Alajlan,et al.  Geometry-Based Image Retrieval in Binary Image Databases , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.