A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms

A new multiple expert fusion algorithm is introduced, designated the “augmented behaviour-knowledge space method”. Most existing multiple expert classification methods rely on a large training dataset in order to be properly utilised. The proposed method effectively overcomes this problem as it exploits the confidence levels of the decisions of each classifier. It will be shown that this new approach is advantageous when small datasets are available, and this is illustrated in its application to the detection of circumscribed masses in digital mammograms, with very encouraging results.

[1]  LamL.,et al.  Application of majority voting to pattern recognition , 1997 .

[2]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[3]  Peter E. Hart,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[4]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .

[5]  D Brzakovic,et al.  An approach to automated detection of tumors in mammograms. , 1990, IEEE transactions on medical imaging.

[6]  Ching Y. Suen,et al.  A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Farzin Deravi,et al.  Evaluating classification strategies for detection of circumscribed masses in digital mammograms , 1999 .

[8]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Ching Y. Suen,et al.  Application of majority voting to pattern recognition: an analysis of its behavior and performance , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[10]  Li WangDong-Chen He,et al.  Texture classification using texture spectrum , 1990, Pattern Recognit..

[11]  Ahmad Fuad Rezaur Rahman,et al.  Generalised approach to the recognition of structurally similar handwritten characters using multiple expert classifiers , 1997 .

[12]  J. Mason,et al.  Algorithms for approximation , 1987 .

[13]  Majid Ahmadi,et al.  Pattern recognition with moment invariants: A comparative study and new results , 1991, Pattern Recognit..

[14]  M Kallergi,et al.  Evaluating the performance of detection algorithms in digital mammography. , 1999, Medical physics.

[15]  Josef Kittler,et al.  Weighting Factors in Multiple Expert Fusion , 1997, BMVC.

[16]  Etta D. Pisano,et al.  Image Processing and Computer Aided Diagnosis in Digital Mammography: A Radiologist's Perspective , 1994 .

[17]  Ahmad Fuad Rezaur Rahman,et al.  Enhancing multiple expert decision combination strategies through exploitation of a priori information sources , 1999 .

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

[19]  Marco Dorigo,et al.  A comparison of Q-learning and classifier systems , 1994 .