Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A level set method based on the Bayesian risk for medical image segmentation
Pattern Recognition
A review of clonal selection algorithm and its applications
Artificial Intelligence Review
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Image Processing
IEEE Transactions on Information Technology in Biomedicine
Clonal selection algorithm for gaussian mixture model based segmentation of 3d brain MR images
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Generalized rough fuzzy c-means algorithm for brain MR image segmentation
Computer Methods and Programs in Biomedicine
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Statistical classification of voxels in brain magnetic resonance (MR) images into major tissue types plays an important role in neuroscience research and clinical practices, in which model estimation is an essential step. Despite their prevalence, traditional techniques, such as the expectation-maximization (EM) algorithm and genetic algorithm (GA), have inherent limitations, and may result in less-accurate classification. In this paper, we introduce the immune-inspired clonal selection algorithm (CSA) to the maximum likelihood estimation of the Gaussian mixture model (GMM), and thus propose the GMM-CSA algorithm for automated voxel classification in brain MR images. This algorithm achieves simultaneous voxel classification and bias field correction in a three-stage iterative process under the CSA framework. At each iteration, a population of admissible model parameters, voxel labels and estimated bias field are updated. To explore the prior anatomical knowledge, we also construct a probabilistic brain atlas for each MR study and incorporate the atlas into the classification process. The GMM-CSA algorithm has been compared to five state-of-the-art brain MR image segmentation approaches on both simulated and clinical data. Our results show that the proposed algorithm is capable of classifying voxels in brain MR images into major tissue types more accurately.