Fusing Markov Random Fields with Anatomical Knowledge and Shape-Based Analysis to Segment Multiple Sclerosis White Matter Lesions in Magnetic Resonance Images of the Brain
نویسندگان
چکیده
This paper proposes an image analysis system to segment multiple sclerosis lesions of magnetic resonance (MR) brain volumes consisting of 3 mm thick slices using three channels (images showing T1-, T2and PD -weighted contrast). The method uses the statistical model of Markov Random Fields (MRF) both at low and high levels. The neighborhood system used in this MRF is defined in three types: (1) Voxel to voxel: a low-level heterogeneous neighborhood system is used to restore noisy images. (2) Voxel to segment: a fuzzy atlas, which indicates the probability distribution of each tissue type in the brain, is registered elastically with the MRF. It is used by the MRF as a-priori knowledge to correct miss-classified voxels. (3) Segment to segment: Remaining lesion candidates are processed by a feature based classifier that looks at unary and neighborhood information to eliminate more false positives. An expert’s manual segmentation was compared with the algorithm.
منابع مشابه
Neural Network-Based Learning Kernel for Automatic Segmentation of Multiple Sclerosis Lesions on Magnetic Resonance Images
Background: Multiple Sclerosis (MS) is a degenerative disease of central nervous system. MS patients have some dead tissues in their brains called MS lesions. MRI is an imaging technique sensitive to soft tissues such as brain that shows MS lesions as hyper-intense or hypo-intense signals. Since manual segmentation of these lesions is a laborious and time consuming task, automatic segmentation ...
متن کاملA Novel Classification Method using Effective Neural Network and Quantitative Magnetization Transfer Imaging of Brain White Matter in Relapsing Remitting Multiple Sclerosis
Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets.Objective: We classified patients with relapsing-r...
متن کاملThe Optimization of Magnetic Resonance Imaging Pulse Sequences in Order to Better Detection of Multiple Sclerosis Plaques
Background and objective: Magnetic resonance imaging (MRI) is the most sensitive technique to detect multiple sclerosis (MS) plaques in central nervous system. In some cases, the patients who were suspected to MS, Whereas MRI images are normal, but whether patients don’t have MS plaques or MRI images are not enough optimized enough in order to show MS plaques? The aim of the current study is ...
متن کاملAutomated Detection of Multiple Sclerosis Lesions Using Texture-based Features and a Hybrid Classifier
Background: Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentati...
متن کاملThe Assessment of Structural Changes in MS Plaques and Normal Appearing White Matter Using Quantitative Magnetization Transfer Imaging (MTI)
Introduction: Multiple sclerosis (MS) is a demyelinating disease of the central nervous system (CNS), affecting mostly young people at a mean age of 30 years. Magnetic resonance imaging (MRI) is one of the most specific and sensitive methods in diagnosing and detecting the evolution of multiple sclerosis disease. But it does not have the ability to differentiate between distinct histopathologic...
متن کامل