Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

Alfiia Galimzianova; Žiga Lesjak; Boštjan Likar; Franjo Pernuš; Žiga Špiclin

doi:10.1117/12.2081642

20 March 2015 Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions

Alfiia Galimzianova, Žiga Lesjak, Boštjan Likar, Franjo Pernuš, Žiga Špiclin

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 94133G (2015) https://doi.org/10.1117/12.2081642
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Alfiia Galimzianova, Žiga Lesjak, Boštjan Likar, Franjo Pernuš, and Žiga Špiclin "Locally adaptive MR intensity models and MRF-based segmentation of multiple sclerosis lesions", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94133G (20 March 2015); https://doi.org/10.1117/12.2081642

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