Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains

Bricq, Stéphanie; Collet, Christophe; Armspach, Jean‐Paul

doi:10.1016/j.media.2008.03.001

Cited by 78 publications

(74 citation statements)

References 32 publications

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“…Van Leemput et al (Van Leemput et al, 2001) proposed a weighted EM algorithm in which the voxels situated far from the model were weighted less in the estimation and considered potential lesions. The trimmed-likelihood estimator (Neykov et al, 2007) has also been used to avoid outliers in the estimation (Aït-Ali et al, 2005;Bricq et al, 2008b;Garcia-Lorenzo et al, 2011). Recently, the integrated square estimation (Scott, 2001) was employed as a cost function because it is a robust estimation method that has the advantage of not having any parameters to tune (Liu et al, 2009).…”

Section: Unsupervised Methodsmentioning

confidence: 99%

“…As in the case of the supervised models described above, several authors using unsupervised methods have proposed to apply MRF to include the local neighborhood in the estimation (Khayati et al, 2008a;Van Leemput et al, 2001). Bricq et al (Bricq et al, 2008b) proposed a double Markov chain that couples information from the neighbor voxels with information from an atlas to improve the segmentation.…”

Section: Unsupervised Methodsmentioning

confidence: 99%

“…One of the segmentation challenges was the segmentation of WML in MS (Styner et al, 2008), and nine methods were compared (Anbeek et al, 2008;Bricq et al, 2008b;García-Lorenzo et al, 2008b;Kroon et al, 2008;Morra et al, 2008;Prastawa and Gerig, 2008;Scully et al, 2008;Shiee et al, 2008;Souplet et al, 2008).…”

Section: Example Validation Framework: Ms Lesion Segmentation Challengementioning

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

319

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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Section: Unsupervised Methodsmentioning

confidence: 99%

Section: Unsupervised Methodsmentioning

confidence: 99%

Section: Example Validation Framework: Ms Lesion Segmentation Challengementioning

confidence: 99%

See 1 more Smart Citation

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

319

268

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show abstract

“…It is implemented and run using its best working parameters. In addition, the proposed algorithm is compared with the recent non-local FCM family of algorithms [13] (NLFCM, NL-R-FCM, and NL-Reg), and Robust Fuzzy C-means algorithm (RFCM) [39], as well as the non-fuzzy methods of expectationmaximization segmentation (EMS) [40], hidden Markov chains (HMC) [41], and statistical parametric mapping (SPM5) [42].…”

Section: Resultsmentioning

confidence: 99%

“…The value of Dice ranges from [0,1], with 0 for no similarity, and 1 for full similarity. The proposed algorithm is compared against a collection of algorithms, including the standard FCM, NFCM, the recent non-local FCM family of algorithms [13] (NLFCM, NL-R-FCM, and NL-Reg), and Robust Fuzzy C-means algorithm (RFCM) [39], as well as the non-fuzzy methods a b c d of expectation-maximization segmentation (EMS) [40], hidden Markov chains (HMC) [41], and statistical parametric mapping (SPM5) [42]. Table 1 lists the average Dice metric on the segmented WM and GM classes for all these algorithms on the T1 BrainWeb database with 20% inhomogeneity under various noise levels.…”

Section: Simulated Normal Mri Segmentationmentioning

confidence: 99%

Tumor segmentation in brain MRI using a fuzzy approach with class center priors

El-Melegy

Mokhtar

2014

J Image Video Proc

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This paper proposes a new fuzzy approach for the automatic segmentation of normal and pathological brain magnetic resonance imaging (MRI) volumetric datasets. The proposed approach reformulates the popular fuzzy c-means (FCM) algorithm to take into account any available information about the class center. The uncertainty in this information is also modeled. This information serves to regularize the clusters produced by the FCM algorithm thus boosting its performance under noisy and unexpected data acquisition conditions. In addition, it also speeds up the convergence process of the algorithm. Experiments using simulated and real, both normal and pathological, MRI volumes of the human brain show that the proposed approach has considerable better segmentation accuracy, robustness against noise, and faster response compared with several well-known fuzzy and non-fuzzy techniques reported in the literature.

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Bayesian Inference and Markov Models

Collet¹

2009

Optimization in Signal and Image Processing

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Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains

Cited by 78 publications

References 32 publications

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

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

Tumor segmentation in brain MRI using a fuzzy approach with class center priors

Bayesian Inference and Markov Models

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