An iterative possibilistic image segmentation system: Application to breast cancer detection

A novel approach for digital mammograms segmentation is proposed. This approach aims to segment the mammograms using an iterative fusion process of information obtained from multiple sources of knowledge (contextual, image processing algorithm, a priori knowledge, etc). Initial Fuzzy Membership Maps (IFMMs) of different thematic classes are first estimated using available information. These IFMM's are then interpreted as Possibility Distribution Maps (PDMs), which represent the possibility for each analyzed pixel to be one of the different thematic classes in the considered image, these possibility values are then iteratively updated using contextual (spatial) information. An additional class called “Rejection” is used to manage ambiguity and to delay the segmentation operation until the establishment of high level possibility degrees for these pixels. The segmentation results are given as a thematic map as well as a confidence curve evaluating the segmentation result quality.

[1]  Arianna Mencattini,et al.  Breast Mass Segmentation in Mammographic Images by an Effective Region Growing Algorithm , 2008, ACIVS.

[2]  Anca L. Ralescu,et al.  Fuzzy Algorithm for , 2004 .

[3]  Hans Burkhardt,et al.  An effective image thresholding method using a fuzzy compactness measure , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[4]  P. Mayo,et al.  Comparing methods to denoise mammographic images , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Lotfi A. Zadeh,et al.  Fuzzy Algorithms , 1968, Inf. Control..

[6]  Didier Dubois,et al.  Possibility theory , 2018, Scholarpedia.

[7]  Fatima Oulebsir Boumghar,et al.  Détection de Lésions Stellaires par la Multirésolution et les Réseaux de Neurones , 2005 .

[8]  Bassel Solaiman,et al.  Multisensor data fusion using fuzzy concepts: application to land-cover classification using ERS-1/JERS-1 SAR composites , 1999, IEEE Trans. Geosci. Remote. Sens..

[9]  Huilong Duan,et al.  A Model-based Algorithm for Mass Segmentation in Mammograms , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[10]  Hong Yan,et al.  A technique of three-level thresholding based on probability partition and fuzzy 3-partition , 2001, IEEE Trans. Fuzzy Syst..

[11]  S. Lai,et al.  On techniques for detecting circumscribed masses in mammograms. , 1989, IEEE transactions on medical imaging.

[12]  Tarik Zouagui,et al.  Approche fonctionnelle générique des méthodes de segmentation d'images , 2004 .

[13]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[14]  Vijay K. Jain,et al.  Markov random field for tumor detection in digital mammography , 1995, IEEE Trans. Medical Imaging.

[15]  Heng-Da Cheng,et al.  Microcalcification detection using fuzzy logic and scale space approaches , 2004, Pattern Recognit..

[16]  V. V. S. Sarma,et al.  Estimation of fuzzy memberships from histograms , 1985, Inf. Sci..

[17]  Arie Tzvieli.  Possibility theory: An approach to computerized processing of uncertainty , 1990, J. Am. Soc. Inf. Sci..

[18]  Heng-Da Cheng,et al.  A novel approach to microcalcification detection using fuzzy logic technique , 1998, IEEE Transactions on Medical Imaging.

[19]  M. M. Anguh,et al.  Multiscale segmentation and enhancement in mammograms , 1997, Proceedings X Brazilian Symposium on Computer Graphics and Image Processing.

[20]  Huilong Duan,et al.  A Novel Computer-Aided Diagnosis System of the Mammograms , 2006 .

[21]  Víctor Giménez-Martínez,et al.  Iterative method for automatic detection of masses in digital mammograms for computer-aided diagnosis , 1999, Medical Imaging.

[22]  Ludovic Roux.  An Application of Possibility Theory Information Fusion to Satellite Image Classification , 1997, Fuzzy Logic in Artificial Intelligence.

[23]  K. Satya Prasad,et al.  AUTOMATIC DETECTION OF BREAST CANCER MASS IN MAMMOGRAMS USING MORPHOLOGICAL OPERATORS AND FUZZY C -MEANS CLUSTERING , 2009 .

[24]  B. Bouchon-Meunier,et al.  La logique floue et ses applications , 1995 .

[25]  Semchedine Moussa,et al.  Système Coopératif de Classification Floue Possibiliste avec Rejet d'Ambiguïté « Application à la segmentation d'images IRM » , 2007 .

[26]  N. N. Karnik,et al.  Introduction to type-2 fuzzy logic systems , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[27]  Didier Dubois,et al.  Possibility theory in information fusion , 2002, NMR.

[28]  K. Inamura,et al.  Segmentation of microcalcification in X-ray mammograms using entropy thresholding , 2002 .

[29]  Monique Rasigni,et al.  METHOD FOR CHARACTERIZING MASSES IN DIGITAL MAMMOGRAMS , 1996 .

[30]  Heng-Da Cheng,et al.  Fuzzy entropy threshold approach to breast cancer detection , 1995 .

[32]  Hiroshi Fujita,et al.  Automated contour extraction of mammographic mass shadow using an improved active contour model , 2004, CARS.

[33]  Martin P. DeSimio,et al.  Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency , 1997, IEEE Transactions on Medical Imaging.

[34]  Han Zhang,et al.  Automated breast masses segmentation in digitized mammograms , 2004, IEEE International Workshop on Biomedical Circuits and Systems, 2004..

[35]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[36]  A. Ahmadian,et al.  Computer-Aided Mass Detection on Digitized Mammograms Using Adaptive Thresholding and Fuzzy Entropy , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[37]  H. D. Cheng,et al.  Automatically Determine the Membership Function Based on the Maximum Entropy Principle , 1997, Inf. Sci..

[38]  Bernadette Bouchon‐Meunier.  Uncertainty Management in Medical Applications , 2012 .

[39]  Heng-Da Cheng,et al.  Approaches for automated detection and classification of masses in mammograms , 2006, Pattern Recognit..

[40]  M.R. El-Sakka,et al.  Fuzzy Entropy Based Detection of Suspicious Masses in Digital Mammogram Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.