Quantitative Comparison of Artificial Honey Bee Colony Clustering and Enhanced SOM based K-means Clustering Algorithms for Extraction of ROI from Breast DCE-MR Images

This paper introduces a comparison of two popular clustering algorithms for breast DCE-MRI segmentation purpose. Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. The goal of breast magnetic resonance image segmentation is to accurately identify the principal mass or lesion structures in these image volumes. There are many methods that exist to segment the breast DCE-MR images. One of these, K-means clustering procedure provides effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas and can segment the breast DCEMRI with high precision. The artificial bee colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. This paper compares the performance of two image segmentation techniques in the subject of breast DCE-MR image. In the experiments, the real dynamic contrast enhanced magnetic resonance images (DCEMRI) are used. Results show that artificial bee colony algorithm performs better in terms of segmentation accuracy, robustness and speed of computation.