Adaptive k-means clustering algorithm for MR breast image segmentation

• Published in: Neural computing & applications Vol. 24; no. 7-8; pp. 1917 - 1928
• Main Authors: Moftah, Hossam M., Azar, Ahmad Taher, Al-Shammari, Eiman Tamah, Ghali, Neveen I., Hassanien, Aboul Ella, Shoman, Mahmoud
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2014; Springer
• Subjects: Applied sciences; Artificial Intelligence; Biological and medical sciences; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Exact sciences and technology; Genital system. Mammary gland; Gynecology. Andrology. Obstetrics; Image Processing and Computer Vision; Investigative techniques, diagnostic techniques (general aspects); Mammary gland diseases; Medical sciences; Memory organisation. Data processing; Original Article; Pattern recognition. Digital image processing. Computational geometry; Probability and Statistics in Computer Science; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Software; Tumors; Adaptive segmentation; Image segmentation; Magnetic resonance (MR) image; Breast cancer; K-means clustering; Cluster analysis; Adaptive algorithm; Methodology; Image processing; Cluster; K means algorithm; Data mining; Nuclear magnetic resonance imaging; Adaptive method; Optimization; Experimental result; Efficiency; Classification; Medical imagery
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-013-1437-4

Image segmentation is vital for meaningful analysis and interpretation of the medical images. The most popular method for clustering is k-means clustering. This article presents a new approach intended to provide more reliable magnetic resonance (MR) breast image segmentation that is based on adaptation to identify target objects through an optimization methodology that maintains the optimum result during iterations. The proposed approach improves and enhances the effectiveness and efficiency of the traditional k-means clustering algorithm. The performance of the presented approach was evaluated using various tests and different MR breast images. The experimental results demonstrate that the overall accuracy provided by the proposed adaptive k-means approach is superior to the standard k-means clustering technique.