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Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging pp 209–222Cite as

Fuzzy Object Growth Model for Neonatal Brain MR Understanding

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 140))

Abstract

This chapter summaries a brain region segmentation method for newborn using magnetic resonance (MR) images. The method deploys fuzzy object growth model (FOGM) which is an extension of fuzzy object model. It is a 4-dimensional model which gives a prior knowledge of brain shape and position at any growing time. First we calculate 4th dimension of FOGM, called growth index in this chapter. Because the growth index will be different from person to person even in the same age group, the method estimates the growth index from cerebral shape using Manifold learning. Using the growth index, FOGM is constructed from the training dataset. To recognize the brain region in evaluating subject, it first estimates the growth index. Then, the brain region is segmented using fuzzy connected image segmentation with the FOGM matched by the growth index. To evaluate the method, this study segments the parenchymal region of 16 subjects (revised age; 0–2 years old) using synthesized FOGM.

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Acknowledgements

Authors would like to express their sincere appreciation to Mr. Ryosuke Nakano, University of Hyogo, JAPAN, who implements the method. This work was supported in part by JSPS Grant-in-Aid for Scientific Research on Innovative Areas (Multidisciplinary Computational Anatomy) JSPS KAKENHI Grant Number 15H01126.

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Authors and Affiliations

  1. Graduate School of Engineering, University of Hyogo, Kobe, Japan

    Saadia Binte Alam & Syoji Kobashi

  2. International University of Business Agriculture and Technology, Dhaka, Bangladesh

    Saadia Binte Alam

  3. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    Jayaram K Udupa

Authors
  1. Saadia Binte Alam
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  2. Syoji Kobashi
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  3. Jayaram K Udupa
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Corresponding author

Correspondence to Syoji Kobashi .

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Editors and Affiliations

  1. Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, Illinois, USA

    Kenji Suzuki

  2. Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Yisong Chen

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Alam, S.B., Kobashi, S., Udupa, J.K. (2018). Fuzzy Object Growth Model for Neonatal Brain MR Understanding. In: Suzuki, K., Chen, Y. (eds) Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging. Intelligent Systems Reference Library, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-68843-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-68843-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68842-8

  • Online ISBN: 978-3-319-68843-5

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