A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI

• Published in: Expert systems with applications Vol. 34; no. 2; pp. 1285 - 1295
• Main Authors: Yeh, Jinn-Yi, Fu, J.C.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2008
• Subjects: Clustering; Hierarchical genetic algorithm (HGA); Learning-vector quantization (LVQ); Magnetic resonance images (MRI); Segmentation; Learning-vector quantization (LVQ); Magnetic resonance images (MRI); Hierarchical genetic algorithm (HGA); Segmentation; Clustering
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2006.12.012

Magnetic resonance imaging (MRI) segmentation has been implemented by many clustering techniques, such as k-means, fuzzy c-means (FCM), learning-vector quantization (LVQ) and fuzzy algorithms for LVQ (FALVQ). Although these algorithms have been successful in applying MRI segmentation, obtaining the right number of clusters and adapting to different cluster characteristics are still not satisfactorily addressed. This report proposes an optimization technique, a hierarchical genetic algorithm with a fuzzy learning-vector quantization network (HGALVQ), to segment multi-spectral human-brain MRI. Evaluation of this approach is based on a real case with human-brain MRI of an individual suffering from meningioma. The HGALVQ is verified by the comparison with other popular clustering algorithms such as k-means, FCM, FALVQ, LVQ, and simulated annealing. Experimental results show that HGALVQ not only returns an appropriate number of clusters and also outperforms other methods in specificity.