Automated Detection and Characterization of Multiple Sclerosis Lesions in Brain MR Images

Goldberg-Zimring, Daniel; Achiron, Anat; Miron, Sébastian; Faibel, Meir; Azhari, Haim

doi:10.1016/s0730-725x(97)00300-7

Cited by 58 publications

(49 citation statements)

References 20 publications

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“…9 To facilitate time-efficient, reproducible, and accurate lesion-load detection, many algorithms have been proposed for fully automated computer-assistive solutions. 3,18 These methods use different principles, including intensity-gradient features, 19 intensity thresholding, 20 intensity-histogram modeling of expected tissue classes, [21][22][23] fuzzy connectedness, 24 identification of nearest neighbors in a feature space, 25,26 or a combination of these. Methods such as Bayesian inference, expectation maximization, support-vector machines, k-nearest neighbor majority voting, and artificial neural networks are algorithmic approaches used to op- Comparing the number of study pairs improved with demyelinating lesions detected by both readers when using the newly developed assistive software to the issued radiology report.…”

Section: Discussionmentioning

confidence: 99%

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

Heerden

Rawlinson

Zhang

et al. 2015

AJNR Am J Neuroradiol

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Section: Discussionmentioning

confidence: 99%

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

Heerden

Rawlinson

Zhang

et al. 2015

AJNR Am J Neuroradiol

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“…In this case, the supervised method is only used as classification algorithm because the region was detected by other means. Goldberg-Zimring et al (Goldberg-Zimring et al, 1998) employed an adaptive thresholding technique to obtain candidate lesions, which included both lesions and other artefacts that resembled lesions. The artefacts were then discarded in a second stage by the ANN using as input the shape and intensity of the candidate lesion.…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

“…Due to the problems faced in obtaining the true boundaries of WML, some authors have proposed to validate the automatic detection of lesions by counting them (Goldberg-Zimring et al, 1998;Sajja et al, 2006;Styner et al, 2008).…”

Section: -Lesion-based Measuresmentioning

confidence: 99%

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Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

García-Lorenzo

Francis

Narayanan

et al. 2013

Medical Image Analysis

315

268

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Magnetic resonance (MR) imaging is often used to characterize and quantify multiple sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have been used to evaluate MS disease burden, to track the progression of the disease and to evaluate the effect of new pharmaceuticals in clinical trials. Accurate identification of MS lesions in MR images is extremely difficult due to variability in lesion location, size and shape in addition to anatomical variability between subjects. Since manual segmentation requires expert knowledge, is time consuming and is subject to intra-and interexpert variability, many methods have been proposed to automatically segment lesions.The objective of this study was to carry out a systematic review of the literature to evaluate the state of the art in automated multiple sclerosis lesion segmentation. From 1240 hits found initially with PubMed and Google scholar, our selection criteria identified 80 papers that described an automatic lesion segmentation procedure applied to MS. Only 47 of these included quantitative validation with at least one realistic image. In this paper, we describe the complexity of lesion segmentation, classify the automatic MS lesion segmentation methods found, and review the validation methods applied in each of the papers reviewed. Although many segmentation solutions have been proposed, including some with promising results using MRI data obtained on small groups of patients, no single method is widely employed due to performance issues related to the high variability of MS lesion appearance and differences in image acquisition. The challenge remains to provide segmentation techniques that work in all cases regardless of the type of MS, duration of the disease, or MRI protocol, and this within a comprehensive, standardized validation framework. MS lesion segmentation remains an open problem.

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“…To handle these difficulties, a large number of approaches have been studied, including fuzzy logic methods [2], neural networks [3], Markov random field methods with the maximum expectation [4], statistical methods [4], and data fusion methods [5], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Segmentation by Multispectral Fusion Approach with Adaptive Operators: Application to Medical Images

Chaabane¹,

Moussaoui²

2011

IJACSA

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Abstract-With the development of acquisition image techniques, more and more image data from different sources of image become available. Multi-modality image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single modality. In medical imaging based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents the evaluation of the segmentation of MR images using the multispectral fusion approach in the possibility theory context . Some results are presented and discussed.

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Automated Detection and Characterization of Multiple Sclerosis Lesions in Brain MR Images

Cited by 58 publications

References 20 publications

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

Improving Multiple Sclerosis Plaque Detection Using a Semiautomated Assistive Approach

Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

Evaluation of the Segmentation by Multispectral Fusion Approach with Adaptive Operators: Application to Medical Images

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