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.
Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov random field (MRF) models are known to give reliable and smooth NABT segmentation. However, the presence of pathology, MR intensity bias and natural tissue-dependent intensity variability altogether represent difficult challenges for a reliable estimation of NABT intensity model based on MR images. In this paper, we propose a novel method for segmentation of normal and pathological structures in brain MR images of multiple sclerosis (MS) patients that is based on locally-adaptive NABT model, a robust method for the estimation of model parameters and a MRF-based segmentation framework. Experiments on multi-sequence brain MR images of 27 MS patients show that, compared to whole-brain model and compared to the widely used Expectation-Maximization Segmentation (EMS) method, the locally-adaptive NABT model increases the accuracy of MS lesion segmentation.
Diagnosis and prognosis of patients with multiple sclerosis (MS) rely on quantitative markers derived from the analysis
of magnetic resonance (MR) images. To compute these markers, a segmentation of lesions in the brain tissues, which are
characteristic for MS disease, is needed. In this paper, we propose an unsupervised method for segmenting MS lesions
that employs localized trimmed-likelihood estimation (TLE) to model the intensity distributions of normal appearing
brain tissues (NABT). Compared to the original whole-brain TLE approach, the proposed method employs a set of three-component
Gaussian mixture models for each of the spatially localized and non-overlapping subregions of the brain. The
subregions were assigned by the customized balanced box decomposition that takes into account the spatial distribution
and the cardinality of NABT tissues, as obtained from the initial whole-brain TLE. The proposed method was tested and
compared to the original TLE approach on publicly available synthetic BrainWeb datasets. The results indicate a higher
average Dice similarity coefficient both for the segmentation of NABT and MS lesions by using the proposed spatially
localized TLE as compared to the original whole-brain TLE, which is due to the fact that the proposed method yields a
more accurate NABT model and thus detects fewer false NABT outliers.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.