Change detection and classification in brain MR images using Change Vector Analysis


Simoes, Rita and Slump, Cornelis (2011) Change detection and classification in brain MR images using Change Vector Analysis. In: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2011, 30 August - 03 September 2011, Boston, Massachusetts USA (pp. pp. 7803-7807).

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Abstract:The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases—such as Alzheimer’s—focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.
Item Type:Conference or Workshop Item
Copyright:© 2011 IEEE
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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