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Multiple Sclerosis (MS) is the most prevalent demyelinating disease of the Central Nervous System, being the Relapsing-Remitting (RRMS) its most common subtype. We explored here the viability of use of Self Organizing Maps (SOM) to perform automatic segmentation of MS lesions apart from CNS normal tissue. SOM were able, in most cases, to successfully segment MRIs of patients with RRMS, with the correct...
In this study, the unsupervised clustering method namely K-means algorithm is applied for identifying the multiple sclerosis (MS) lesions in magnetic resonance (MR) images automatically. MS lesion detection is essential for diagnosing the disease and monitoring its progression. The automated method aims to eliminate user-dependent classification errors and to improve computational capacity in detecting...
Cortical Thickness (CTh) estimation from Magnetic Resonance Imaging (MRI) data of Multiple Sclerosis (MS) patients is biased at variable extent by the presence of white matter lesions. To overcome this limitation, several methods have been developed. In this study, we evaluate the impact on CTh measurements of different lesion corrections obtained combining three lesion segmentations (manual or automatic)...
Multiple Sclerosis (MS) is a central nervous system (CNS) disorders resulting from damage to the myelin sheath which helps to ensure the transmission of messages between the brain and spinal cord. MS lesions occur in patients with damage to the myelin sheath. Progression of MS lesions is important for examining the disease. MS lesions often Magnetic Resonance Imaging (MRI) is determined and planned...
With recent advances in the field, magnetic resonance imaging (MRI) has become a powerful quantitative imaging modality for the study of neurological disorders. The quantitative power of MRI is significantly enhanced with multi-contrast and high-resolution techniques. However, those techniques generate large volumes of data which, combined with the sophisticated state-of-the-art image analysis methods,...
Sparse representations allow modeling data using a few basis elements of an over-complete dictionary and have been used in many image processing applications. We propose to use a sparse representation and an adaptive dictionary learning paradigm to automatically classify Multiple Sclerosis (MS) lesions from MRI. In particular, we investigate the effects of learning dictionaries specific to the lesions...
This paper presents an automatic algorithm for the detection of multiple sclerosis lesions (MSL) from multi-sequence magnetic resonance imaging (MRI). We build a probabilistic classifier that can recognize MSL as a novel class, trained only on Normal Appearing Brain Tissues (NABT). Patch based intensity information of MRI images is used to train a classifier at the voxel level. The classifier is in...
This paper proposes an approach to automatically segment MS lesions in MR images using fuzzy c-means (FCM) and a support vector machines (SVM) based on the sequential minimal optimization (SMO) in learning step. A postprocessing based on morphological operations was applied to refine the obtained results. The proposed approach was tested on 3D MR images and the obtained results are encouraging.
Multiple sclerosis is a chronic inflammatory disease of the central nervous system. Lesions detected by Magnetic resonance (MR) sequences not only confirme the diagnosis of MS, but let monitor them to determine the evolutionary state of the disease and to evaluate the therapeutic efficiency. Thus, the change in lesion load is a criterion determining the degree of progress of the disease in volume,...
Detecting abnormalities in medical images is one application of image segmentation. MRI as an imaging technique sensitive to soft tissues such as brain shows Multiple Scleroses 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 brain lesion segmentation have been proposed. To tackle difficulties...
Magnetic resonance (MR) images can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted from its corresponding T1-w image to get an image in which the cerebral spinal fluid (CSF)...
The detection of multiple sclerosis lesion is important for many neuroimaging studies. In this paper, a new automatic algorithm for lesion segmentation based on the multi-channel MR images (T1w, T2w and FLAIR image) is proposed, which utilizes the unique and complementary intensity information of multi-channel MR images. In this method, the observed multi-channel MR images are modeled as a vector...
This paper proposes a longitudinal intensity normalization algorithm for T1-weighted magnetic resonance images of human brains in the presence of multiple sclerosis lesions, aiming towards stable and consistent longitudinal segmentations. Unlike previous longitudinal segmentation methods, we propose a 4D intensity normalization that can be used as a preprocessing step to any segmentation method. The...
Segmentation is an important step for the diagnosis of multiple sclerosis. This paper presents a new approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. At first, Brain image is considered to be three parts, namely the dark, the gray, and the white part. Then, the fuzzy regions of their member functions are determined...
We present a new automatic method for segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Images. The algorithm performs tissue classification combining a within subject global tissue intensity model and a local tissue intensity model derived from an aligned set of healthy reference subjects. MS lesions are detected as outliers towards the proposed coupled global/local intensity...
The large number of false positives that result when automatic algorithms are considered for segmenting small multiple sclerosis lesions in magnetic resonance imaging hampers the posterior evaluation of lesion load. To address this problem we propose a fuzzy system which can improve the differentiation between true and false positive detections in proton density- and T2-weighted images. On the basis...
In this study, a fuzzy clustering method has been proposed in order to segment brain tissues affected by the multiple sclerosis (MS). In traditional fuzzy clustering, the neighboring relations between pixels have not been taken account of. Additionally, the performance of the clustering reduces drastically because of the pixels having close gray levels due to noise. Therefore, in this study, a novel...
Intensity based classification relies on contrast between tissue types adjacent in feature space and adequate signal compared to image noise. Contrast between brain tissue types in Multiple Sclerosis patients Magnetic Resonance Imaging is reduced due to the presence of lesions which intensity values overlap with healthy tissue, resulting in tissue misclassification. We propose a new, extended classifier...
Diffusion tensor imaging provides rich information about human brain connectivity in vivo, yet most current methods for fiber tractography or tract segmentation do not address white matter pathologies such as multiple sclerosis lesions, which can alter the diffusion tensor characteristics. We study here the effects of MS lesions on estimated diffusion tensors and how they affect the processing of...
We present a new automatic method for segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. The method performs tissue classification using a model of intensities of the normal appearing brain tissues. In order to estimate the model, a trimmed likelihood estimator is initialized with a hierarchical random approach in order to be robust to MS lesions and other outliers present...
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