Using SPM to Detect Evolving MS Lesions

Rey, David; Stoeckel, Jonathan; Malandain, Grégoire; Ayache, Nicholas

doi:10.1007/3-540-45468-3_175

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Another way to analyze these curves lies in their evaluation relatively to a curve of intensity variation linked to a voxel included in a lesion. This approach promotes the identification of false positives [6]. Subsequently, it becomes practical to prototype the evolution of the intensity of a lesion voxel to automate the recognition of a similar in various sequences.…”

Section: Selective Segmentationmentioning

confidence: 99%

Brief review of multiple sclerosis lesions segmentation methods on conventional magnetic resonance imaging

Ghribi

Njeh

Hamida

et al. 2014

2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

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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, shape and location. For this purpose, a segmentation of these lesions becomes paramount. Some recent methods of semiautomatic and automatic segmentation have been proposed to get rid of complex and laborious manual segmentation. Subsequently, the variability inter and intra-experts will be reduced.The purpose of this study is to accomplish a brief review of MS lesions segmentation methods proposed in the literature.

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Section: Selective Segmentationmentioning

confidence: 99%

Brief review of multiple sclerosis lesions segmentation methods on conventional magnetic resonance imaging

Ghribi

Njeh

Hamida

et al. 2014

2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

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“…-Evolutive lesions in SEP lesions can be detected in a time series of 3D images [RSMA01] (4D image) by correlating the temporal signal of each voxel with an evolving lesion model. Before conducting any statistical analysis, the images have to be normalized in intensity.…”

Section: Introductionmentioning

confidence: 99%

Intensity Compensation within Series of Images

Malandain

Bardinet

2003

Lecture Notes in Computer Science

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Abstract. This article addresses the problem of histogram matching in the context of medical image processing. Such a problem occurs while comparing two images of the same object, where intensity differences are due to different acquisition conditions. This can be compensated by histogram matching or equalization. To achieve this, we based our method on windowing techniques. This allows to match implicitly continuous probability density functions, yielding more robust results than the methods issued from discrete histograms.

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