Abstract—In this paper, a novel approach has been proposed for mass diagnosis in mammography images. The objective is developing a trustable mammography Computer Aided-Diagnosis (CADx) system utilizing a new cognitive classifier. The input Region of Interest (ROI) is subjected to some preprocessing stages; then, a group of features describing the shape, margin and density characteristics of masses have been extracted. The proposed features are consistent with the evaluations that an expert radiologist takes into account in diagnosis process. The most effective features are selected in the feature selection stage and mapped from the set of real numbers to a set of linguistic terms. The proposed classifier primes a knowledge-base which is developed according to a mammography expert; its rules have been written using a special kind of linguistics and grammar formalism. The semantic comparison of features of the image to the expectations of the knowledge base, called cognitive resonance, leads to the final assessment. Since the output of this system comes with reason, the system is trustable. The best achieved Accuracy and False Positive Rate (FPR) are 87.93% and 10.52%, respectively. More numerical results are reported in the paper.

Index Terms—Cognitive pattern recognition, cognitive resonance, computer aided diagnosis, mammography.

A. Tahmasbi is with the Department of Electrical Engineering, University of Texas at Dallas, Texas, U.S. (e-mail: a.tahmasbi@utdallas.edu).
F. Saki and S. B. Shokouhi are with the Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran,Iran (e-mail: f_saki@elec.iust.ac.ir, bshokouhi@iust.ac.ir).

Cite: Amir Tahmasbi, Fatemeh Saki, Abdollah Amirkhani, Seyed Mohammad Seyedzade, and Shariar B.Shokouhi, "Classification of Breast Masses based on Cognitive Resonance," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 283-287, 2012.