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Unsupervised segmentation for MR brain images

Published: 26 October 2011 Publication History

Abstract

As described herein, we propose an unsupervised method for segmentation of magnetic resonance (MR) brain images by hybridizing the self-mapping characteristics of 1-D Self-Organizing Maps (SOMs) and using incremental learning functions of fuzzy Adaptive Resonance Theory (ART). As the proposed method requires the appropriate parameters to segment tissues (such as cerebrospinal fluid, gray matter and white matter) that are necessary for brain atrophy diagnosis, first we derive the optimal parameter set through the preliminary experiments. The main contribution of this work is to evaluate the effectiveness of the proposed method, considering the conventional methods that are highly accurate in terms of usefulness as classification techniques. We focus on Fuzzy C-means (FCM) and Expectation Maximization Gaussian Mixture (EM-GM) with previous setting of the number of clusters, and then Mean Shift (MS) without previous setting of the number of clusters. Through the comparative experiments on the two metrics, we confirmed that our method could achieve higher accuracy than these conventional methods. Additionally, we propose a Computer-Aided Diagnosis (CAD) system for use with brain dock examinations based on case analyses of diagnostic reading. We construct a prototype system for reducing loads on diagnosticians during quantitative analysis of the degree of brain atrophy. Field tests of 193 examples of brain dock medical examinees reveal that the system efficiently supports diagnostic work in the clinical field: the alteration of brain atrophy attributable to aging can be quantified easily, irrespective of the diagnostician.

References

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W. E. Reddick, J. O. Glass, E. N. Cook, T. D. Elkin, and R. J. Deaton, "Automated Segmentation and Classification of Multispectral MagneticResonance Images of Brain using Artificial Neural Networks," IEEE Trans. Med. Imaging, vol. 16, no. 6, pp. 911--918, 1997.
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J. Alirezac, M. E. Jernigan, and C. Nahmias, "Automatic segmentation of MR images using self organizing feature mapping and neural networks," Proceedings of the SPIE The International Society for Optical Engineering, pp. 138--149, 1997.
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  • (2016)Automatic brain segmentation method based on supervoxels2016 International Conference on Systems, Signals and Image Processing (IWSSIP)10.1109/IWSSIP.2016.7502713(1-4)Online publication date: May-2016

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  1. Unsupervised segmentation for MR brain images

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    ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
    October 2011
    949 pages
    ISBN:9781450309134
    DOI:10.1145/2093698
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Universitat Pompeu Fabra
    • IEEE
    • Technical University of Catalonia Spain: Technical University of Catalonia (UPC), Spain
    • River Publishers: River Publishers
    • CTTC: Technological Center for Telecommunications of Catalonia
    • CTIF: Kyranova Ltd, Center for TeleInFrastruktur

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    New York, NY, United States

    Publication History

    Published: 26 October 2011

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    Author Tags

    1. MR image
    2. brain dock examination
    3. segmentation
    4. unsupervised NN

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    • Technical University of Catalonia Spain
    • River Publishers
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    • CTIF

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    • (2016)Automatic brain segmentation method based on supervoxels2016 International Conference on Systems, Signals and Image Processing (IWSSIP)10.1109/IWSSIP.2016.7502713(1-4)Online publication date: May-2016

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