Structure segmentation and recognition in images guided by structural constraint propagation

In some application domains, such as medical imaging, the objects that compose the scene are known as well as some of their properties and their spatial arrangement. We can take advantage of this knowledge to perform the segmentation and recognition of structures in medical images. We propose here to formalize this problem as a constraint network and we perform the segmentation and recognition by iterative domain reductions, the domains being sets of regions. For computational purposes we represent the domains by their upper and lower bounds and we iteratively reduce the domains by updating their bounds. We show some preliminary results on normal and pathological brain images.

[1]  Isabelle Bloch,et al.  Integration of fuzzy spatial relations in deformable models - Application to brain MRI segmentation , 2006, Pattern Recognit..

[2]  Isabelle Bloch,et al.  3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models , 2009, Fuzzy Sets Syst..

[3]  Isabelle Bloch,et al.  A New Fuzzy Connectivity Class Application to Structural Recognition in Images , 2008, DGCI.

[4]  Isabelle Bloch,et al.  Combining Radiometric and Spatial Structural Information in a New Metric for Minimal Surface Segmentation , 2007, IPMI.

[5]  L. Yperzeele,et al.  A.R. Crossman D. Neary , 2010, Clinical Neurology and Neurosurgery.

[6]  Gérard Ligozat,et al.  Reasoning about Cardinal Directions , 1998, J. Vis. Lang. Comput..

[7]  Timos K. Sellis,et al.  Topological relations in the world of minimum bounding rectangles: a study with R-trees , 1995, SIGMOD '95.

[8]  Jean-Michel Jolion,et al.  Adaptive Pyramid and Semantic Graph: Knowledge Driven Segmentation , 2005, GbRPR.

[9]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[10]  Albert A. Michelson,et al.  Studies in Optics , 1995 .

[11]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[12]  Bernhard Nebel,et al.  On the Complexity of Qualitative Spatial Reasoning: A Maximal Tractable Fragment of the Region Connection Calculus , 1999, Artif. Intell..

[13]  Olivier Lhomme,et al.  Consistency Techniques for Numeric CSPs , 1993, IJCAI.

[14]  Toby Walsh,et al.  Handbook of Constraint Programming , 2006, Handbook of Constraint Programming.

[15]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[16]  Isabelle Bloch,et al.  Spatial reasoning under imprecision using fuzzy set theory, formal logics and mathematical morphology , 2006, Int. J. Approx. Reason..

[17]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[18]  Didier Dubois,et al.  Fuzzy sets and systems ' . Theory and applications , 2007 .

[19]  Azriel Rosenfeld,et al.  Fuzzy Digital Topology , 1979, Inf. Control..

[20]  Vladimir Kolmogorov,et al.  Computing geodesics and minimal surfaces via graph cuts , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[21]  Alan K. Mackworth.  Consistency in Networks of Relations , 1977, Artif. Intell..