Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions

Alfiia Galimzianova; Ziga Lesjak; Daniel L. Rubin; Boštjan Likar; Franjo Pernuš; Žiga Špiclin

doi:10.1117/1.JMI.5.1.011007

1 November 2017 Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions

Alfiia Galimzianova, Ziga Lesjak, Daniel L. Rubin, Boštjan Likar, Franjo Pernuš, Žiga Špiclin

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 1, 011007 (November 2017). https://doi.org/10.1117/1.JMI.5.1.011007

Abstract

Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy brains, the NABS can be accurately captured by MR intensity mixture models, which, in combination with regularization techniques, such as in Markov random field (MRF) models, are known to give reliable NABS segmentation. However, on MR images that also contain abnormalities such as MS lesions, obtaining an accurate and reliable estimate of NABS intensity models is a challenge. We propose a method for automated segmentation of normal-appearing and abnormal structures in brain MR images that is based on a locally adaptive NABS model, a robust model parameters estimation method, and an MRF-based segmentation framework. Experiments on multisequence brain MR images of 30 MS patients show that, compared to whole-brain MR intensity model and compared to four popular unsupervised lesion segmentation methods, the proposed method increases the accuracy of MS lesion segmentation.

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Alfiia Galimzianova, Ziga Lesjak, Daniel L. Rubin, Boštjan Likar, Franjo Pernuš, and Žiga Špiclin "Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions," Journal of Medical Imaging 5(1), 011007 (1 November 2017). https://doi.org/10.1117/1.JMI.5.1.011007

Received: 30 June 2017; Accepted: 9 October 2017; Published: 1 November 2017

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