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False Positives Reduction on Segmented Multiple Sclerosis Lesions Using Fuzzy Inference System by Incorporating Atlas Prior Anatomical Knowledge: A Conceptual Model

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 8733)

Abstract

Detecting abnormalities in medical images is an important application of medical imaging. MRI as an imaging technique sensitive to soft tissues shows Multiple Sclerosis (MS) lesions as hyper-intense or hypo-intense signals. As manual segmentation of these lesions is a laborious and time consuming task, many methods for automatic MS lesion segmentation have been proposed. Because of inherent complexities of MS lesions together with acquisition noises and inaccurate pre-processing algorithms, automatic segmentation methods come up with some False Positives (FP). To reduce these FPs a model based on fuzzy inference system by incorporating atlas prior anatomical knowledge have been proposed. The inputs of proposed model are MRI slices, initial lesion mask, and atlas information. In order to mimic experts inferencing, proper linguistic variable are derived from inputs for better description of FPs. The experts knowledge is stored into knowledge-base in if-then like statement. This model can be developed and attached as a module to MS lesion segmentation methods for reducing FPs.

Keywords

  • multiple sclerosis lesion
  • segmentation
  • false positive reduction
  • fuzzy inference system
  • atlas anatomical knowledge
  • MRI
  • MS

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Khastavaneh, H., Haron, H. (2014). False Positives Reduction on Segmented Multiple Sclerosis Lesions Using Fuzzy Inference System by Incorporating Atlas Prior Anatomical Knowledge: A Conceptual Model. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-11289-3_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11288-6

  • Online ISBN: 978-3-319-11289-3

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