Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images

Shahvaran, Zahra; Kazemi, Kamran; Helfroush, Mohammad Sadegh; Jafarian, Nassim; Noorizadeh, Negar

doi:10.1016/j.jneumeth.2012.06.012

Cited by 21 publications

(18 citation statements)

References 33 publications

(54 reference statements)

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“…The work in Ref. 29 was inspired by our previous work in Ref. 22 where the MRF prior was incorporated into the energy term of local region-based active contour (RAC) model.…”

Section: Introductionmentioning

confidence: 99%

Connecting Markov random fields and active contour models: application to gland segmentation and classification

Monaco

Sparks

et al. 2017

J. Med. Imag

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Abstract. We introduce a Markov random field (MRF)-driven region-based active contour model (MaRACel) for histological image segmentation. This Bayesian segmentation method combines a region-based active contour (RAC) with an MRF. State-of-the-art RAC models assume that every spatial location in the image is statistically independent, thereby ignoring valuable contextual information among spatial locations. To address this shortcoming, we incorporate an MRF prior into energy term of the RAC. This requires a formulation of the Markov prior consistent with the continuous variational framework characteristic of active contours; consequently, we introduce a continuous analog to the discrete Potts model. Based on the automated segmentation boundary of glands by MaRACel model, explicit shape descriptors are then employed to distinguish prostate glands belonging to Gleason patterns 3 (G3) and 4 (G4). To demonstrate the effectiveness of MaRACel, we compare its performance to the popular models proposed by Chan and Vese (CV) and Rousson and Deriche (RD) with respect to the following tasks: (1) the segmentation of prostatic acini (glands) and (2) the differentiation of G3 and G4 glands. On almost 600 prostate biopsy needle images, MaRACel was shown to have higher average dice coefficients, overlap ratios, sensitivities, specificities, and positive predictive values both in terms of segmentation accuracy and ability to discriminate between G3 and G4 glands compared to the CV and RD models.

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“…The work in Ref. 29 was inspired by our previous work in Ref. 22 where the MRF prior was incorporated into the energy term of local region-based active contour (RAC) model.…”

Section: Introductionmentioning

confidence: 99%

Connecting Markov random fields and active contour models: application to gland segmentation and classification

Monaco

Sparks

et al. 2017

J. Med. Imag

View full text Add to dashboard Cite

show abstract

“…tering [5], surface fitting [6], histogram analysis [7,8], or segmentation [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] methods. Among these retrospective methods, segmentation-based techniques are the most popular, as they allow segmentation and bias correction to be combined in an iterative process, thereby achieving optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

“…These segmentationbased approaches are either local or global region-based methods. Because of the slowly varying bias field, existing local regionbased methods [10][11][12][13][14][15][16][17] assume the bias fields to be almost constant within a small window. Li et al [10] proposed a variational level-set method (VLS) to simultaneously perform multiphase segmentation and bias correction.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, local region-based approaches tend to be computationally complex, as they calculate local information for each pixel in an image. Statistical methods for local regions rely on the assumption that each pixel in each region is independent, ignoring the spatial correlation between pixels [16]. Wang proposed a spatial information system using a patch-based sparse representation method; however, this is a supervised method that requires a training database [17].…”

Section: Introductionmentioning

confidence: 99%

“…Since accurate classifiers rely on good training databases, supervised methods should cover all possible cases, and are thus difficult to attain [25]. Shahvaran et al [16] utilized a Markov random field (MRF) to model the spatial correlation between neighboring pixels/voxels. Their unsupervised model does not require training data, though it retains the disadvantages of local region-based methods.…”

Section: Introductionmentioning

confidence: 99%

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A fast and robust segmentation of magnetic resonance brain images using a combination of the pyramidal approach and level set method

Belgrana

Benamrane

2016

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We propose in this article an approach to optimize the processing time and to improve the quality of brain magnetic resonance images segmentation. Level set method (LSM) was adopted with a periodic reinitialization process to prevent the LS function from being too steep or too flat near the interface. Although it is used to maintain the stability of the interface evolution and gives interesting results, it requires a longer processing time. To overcome this disadvantage and reduce the processing time, we propose a hybridization with a regular Gaussian pyramid, which reduces the resolution of the initial image and prevents the possibility of local minima. To compare the different segmentation algorithms, we used six types of quality measurements: specificity, sensitivity, Dice similarity, the Jaccard index, and the correctly and incorrectly marked pixels. A comparison between the results obtained by LSM, LSM with reinitialization, the approach of Barman et al., An International Journal 1 (2011), particle swarm optimization based on the Chan and Vese model (Mandal et al., Engineering Applications of Artificial Intelligence 35 (2014), 199-214) and by our hybrid approach reveals a clear efficiency of our hybridization strategy. The processing time was significantly reduced, and the quality of segmentation was improved.

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Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images

Cited by 21 publications

References 33 publications

Connecting Markov random fields and active contour models: application to gland segmentation and classification

Connecting Markov random fields and active contour models: application to gland segmentation and classification

Unsupervised segmentation of noisy and inhomogeneous images using global region statistics with non-convex regularization

A fast and robust segmentation of magnetic resonance brain images using a combination of the pyramidal approach and level set method

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