Development of Intelligent System Based on Artificial Swarm Bee Colony Clustering Algorithm for Efficient Mass Extraction from Breast DCE-MR Images

The high incidence of breast cancer in women has increased significantly in the recent years. Breast cancer is one of the major causes of death among women. Primary prevention seems impossible since the causes of this disease are still remaining unknown. An improvement of early diagnostic techniques is critical for women's quality of life. Early detection of breast cancer is one of the most important factors in determining prognosis for women with malignant tumors. Magnetic Resonance Imaging (MRI) has been shown to be the most sensitive modality for screening high-risk women. Computer Assisted Evaluation (CAE) systems have the potential to assist radiologists in the early detection of cancer. The ability to improve diagnostic information from medical images can be enhanced by designing computer processing algorithms; In order to improve the masses segmentation and overcome the defects of the conventional methods, an intelligent algorithm is proposed to detect cancer in breast Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI). It is well known that k-means algorithm is one of the most widely used clustering techniques. However, solutions of k-means algorithm depend on the initialization of cluster centers and the final solution converges to local minima. The proposed algorithm is designed to overcome k- means algorithm shortcomings. This paper presents a research on Dynamic Contrast Enhanced Magnetic Resonance Images of Breast using artificial honey bee colony algorithm based clustering for breast mass segmentation. The first step of the cancer signs detection should be a segmentation procedure able to distinguish masses and micro calcifications from background tissue using the swarm intelligence algorithm based on honey bee colony clustering has been implemented for intensity - based segmentation. The proposed technique shows better results. The obtained results of this work show that this proposed artificial bee colony algorithm has better performance than other clustering methods.