Abstract. Focal brain lesions are a consequence of head trauma, cerebral infarcts or intracerebral hemorrhages. In clinical practice, magnetic resonance imaging (MRI) is commonly used to reveal them. The segmentation task consists of find-ing the lesion borders. This problem is non-trivial because the lesion may be con-nected to other intracranial compartments with similar intensities. A new method for the automatic segmentation of unilateral lesions is proposed here. The signal statistics of multichannel MR are examined w.r.t. the first-order mirror symmetry of the brain. The algorithm is discussed in detail, and its properties are evaluated on synthetic and real MRI data.
Magnetic Resonance (MR) white matter hyperintensities have been shown to predict an increased risk o...
Image segmentation refers to the process of partitioning a digital image into multiple sets of pixel...
AbstractThe aim of this work is to propose the fully automated pathological area extraction from mul...
Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple scl...
Abstract Automatic brain abnormality detection is a major challenge in medical image processing. Man...
International audienceIn this paper, we present a new automatic segmentation method for magnetic res...
Automated segmentation of brain lesions in magnetic resonance images (MRI) is a difficult procedure ...
MRI Brain image segmentation is a valuable tool in the diagnosis and treatment of many different typ...
A method to automatically segment cerebrospinal fluid, gray matter, white matter and white matter le...
Multiple Sclerosis (MS) is a neurodegenerative disease that is associated with brain tissue damage p...
In this paper, we present a new automatic segmentation me-thod for magnetic resonance images. The ai...
Purpose: Automatic brain-lesion segmentation has the potential to greatly expand the analysis of the...
This paper presents an automatic lesion segmentation method based on similarities between multichann...
Image segmentation refers to the process of partitioning a digital image into multiple sets of pixel...
A method to automatically segment cerebrospinal fluid, gray matter, white matter and white matter le...
Magnetic Resonance (MR) white matter hyperintensities have been shown to predict an increased risk o...
Image segmentation refers to the process of partitioning a digital image into multiple sets of pixel...
AbstractThe aim of this work is to propose the fully automated pathological area extraction from mul...
Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple scl...
Abstract Automatic brain abnormality detection is a major challenge in medical image processing. Man...
International audienceIn this paper, we present a new automatic segmentation method for magnetic res...
Automated segmentation of brain lesions in magnetic resonance images (MRI) is a difficult procedure ...
MRI Brain image segmentation is a valuable tool in the diagnosis and treatment of many different typ...
A method to automatically segment cerebrospinal fluid, gray matter, white matter and white matter le...
Multiple Sclerosis (MS) is a neurodegenerative disease that is associated with brain tissue damage p...
In this paper, we present a new automatic segmentation me-thod for magnetic resonance images. The ai...
Purpose: Automatic brain-lesion segmentation has the potential to greatly expand the analysis of the...
This paper presents an automatic lesion segmentation method based on similarities between multichann...
Image segmentation refers to the process of partitioning a digital image into multiple sets of pixel...
A method to automatically segment cerebrospinal fluid, gray matter, white matter and white matter le...
Magnetic Resonance (MR) white matter hyperintensities have been shown to predict an increased risk o...
Image segmentation refers to the process of partitioning a digital image into multiple sets of pixel...
AbstractThe aim of this work is to propose the fully automated pathological area extraction from mul...