MEDICAL IMAGE PROCESSING BY MEANS OF SOME ARTIFICIAL INTELLIGENCE METHODS

Medical images are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease progression. Technically, medical imaging mainly processes uncertain, missing, ambiguous, complementary, inconsistent, redundant contradictory, distorted data and information has a strong structural character. As a general approach, the understanding of any image involves the matching of features extracted from the image with pre?stored models. The production of a h igh?level symbolic model requires the representation of knowledge about the objects to be modeled, their relationships, and how and when to use the information stored within the model. This paper reports new (semi)automated methods for the segmentation and classification of medical images using artificial intelligence, i.e . soft computing techniques ( e.g . fuzzy logic and genetic algorithms), information fusion and specific domain knowledge. Fuzzy logic acts as a unified framework for representing and processing both numerical and symbolic information ("hybridization"), as well as structural information constituted mainly by spatial relationships in biomedical imaging. Promising results show the superiority of the soft computing and knowledge?based approach ove r best traditional techniques in terms of segmentation errors. The classification of different anatomic structures is made by implementing rules yielded both by domain

[1]  Akshay K. Singh,et al.  Deformable models in medical image analysis , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[2]  Cristian Rotariu,et al.  Tremor Assessment by Means of Handscript Analysis and Fuzzy Logic , 2001 .

[3]  Milan Sonka,et al.  Image processing analysis and machine vision [2nd ed.] , 1999 .

[4]  Dorin Comaniciu,et al.  Image-guided decision support system for pathology , 1999, Machine Vision and Applications.

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

[6]  Tony F. Chan,et al.  Image processing and analysis , 2005 .

[7]  P. Bahr,et al.  Sampling: Theory and Applications , 2020, Applied and Numerical Harmonic Analysis.

[8]  Yehoshua Y. Zeevi,et al.  Neural networks: theory and applications , 1992 .

[9]  Lakhmi C. Jain,et al.  Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications , 1998 .

[10]  Hariton Costin.  Plenary lecture VII: biomedical image processing & analysis via artificial intelligence and information fusion , 2008 .

[11]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .

[12]  Jean Dezert,et al.  Applications and Advances of DSmT for Information Fusion , 2004 .

[13]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[14]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[15]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[16]  Hariton Costin,et al.  Knowledge-based contour detection in medical imaging using fuzzy logic , 2003, Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on.

[17]  Bir Bhanu,et al.  Adaptive image segmentation using a genetic algorithm , 1989, IEEE Transactions on Systems, Man, and Cybernetics.

[18]  Silviu Ioan Bejinariu,et al.  Medical Image Registration by means of a Bio-Inspired Optimization Strategy , 2012, Comput. Sci. J. Moldova.

[19]  Rita Cucchiara,et al.  Tuning Range Image Segmentation by Genetic Algorithm , 2003, EURASIP J. Adv. Signal Process..

[20]  Didier Dubois,et al.  Readings in Fuzzy Sets for Intelligent Systems , 1993 .

[21]  Hariton Costin,et al.  A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis , 2013, Int. J. Comput. Commun. Control.

[22]  James C. Bezdek,et al.  Clustering with a genetically optimized approach , 1999, IEEE Trans. Evol. Comput..

[23]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .