An Extension of the Standard Mixture Model for Image Segmentation

Nguyen, Thanh Minh; Wu, Qi; Ahuja, Siddhant

doi:10.1109/tnn.2010.2054109

Cited by 58 publications

(4 citation statements)

References 36 publications

(79 reference statements)

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“…A spatial constraint method for the Gaussian mixture model was proposed [21] based on the extended Gaussian mixture model (EGMM), which is used to construct the neighbourhood information weight function with the prior probability to constrain the pixel space and improve the noise robustness of the GMM.…”

Section: Feature Selection Algorithm With a Fuzzy Gaussian Mixture Momentioning

confidence: 99%

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

Ren

2020

Sensors

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This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback–Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means (FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model (SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272–12.9803 dB, 1.5501–13.4396 dB, 1.9113–11.2613 dB and 1.0233–10.2804 dB over the other methods, and the Misclassification rate (MSR) decreases by 0.32–37.32%, 5.02–41.05%, 0.3–21.79% and 0.9–30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation.

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Section: Feature Selection Algorithm With a Fuzzy Gaussian Mixture Momentioning

confidence: 99%

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

Ren

2020

Sensors

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“…Recent literature on medical imaging, on the other hand, reports multi‐modality as a key feature of intensity distribution as opposed to the uni‐modality (Kawaguchi & Yamashita, 2017). For example, Greenspan et al (2006) modelled each data cluster by mixture of large number of Gaussian distributions to incorporate multimodality of an image class, while Nguyen et al (2010) extended the standard Gaussian mixture model where the probability of each class varies over different pixels and is dependent on the neighbouring pixels. Ozenne et al (2015) used an unsupervised multivariate segmentation algorithm that incorporates spatial information based on four‐class Gaussian mixture model for classification of white matter diseased lesion in elderly patients.…”

Section: Introductionmentioning

confidence: 99%

Characterization of a robust probabilistic framework for brain magnetic resonance image data distributions

Banerjee

Mukhoti

2023

Stat

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Probabilistic characterisation of image data for accurate prognosis and treatment planning remains a long‐standing problem in medical research, especially when the data distribution depicts flat‐top and high‐order contact. Such flat‐top distributions are quite common in brain magnetic resonance (MR) image data, where the density drops sharply beyond the flat interval. Intuitively, it would indicate a bipartition of data into positive region containing observations definitely belonging to the image class and boundary region with observations possibly belonging to it. The flat peak would also imply that multiple values are equally most likely to belong to that class. However, the popular probability distributions used in such cases are unimodal, creating ambiguity about the positive region. In this work, we study the statistical properties and develop likelihood‐based iterative estimation method for the parameters of a novel class of platykurtic probability distributions containing normal, called the stomped normal distribution, that provides more accurate modelling to the flat‐top data distributions. The robustness of the proposed stomped normal model has been illustrated with six simulated and nine real brain MR volumes. Our analysis shows substantial improvement in explaining a variety of shapes of data distributions using the proposed probability model.

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“…Instead of imposing the MRF-based constraint on the pixel labels, some other approaches directly impose spatial constraints on contextual mixing proportions and take into account the spatial correlation of pixels. [21][22][23] However, in these approaches, the prior distribution is different for each pixel and depends on the neighbors of the pixel. This limitation makes them lost global cluster information.…”

Section: Introductionmentioning

confidence: 99%

A coarse-to-fine scheme for groupwise registration of multisensor images

Zhu

et al. 2016

International Journal of Advanced Robotic Systems

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Ensemble registration is concerned with a group of images that need to be registered simultaneously. It is challenging but important for many image analysis tasks such as vehicle detection and medical image fusion. To solve this problem effectively, a novel coarse-to-fine scheme for groupwise image registration is proposed. First, in the coarse registration step, unregistered images are divided into reference image set and float image set. The images of the two sets are registered based on segmented region matching. The coarse registration results are used as an initial solution for the next step. Then, in the fine registration step, a Gaussian mixture model with a local template is used to model the joint intensity of coarse-registered images. Meanwhile, a minimum message length criterion-based method is employed to determine the unknown number of mixing components. Based on this mixture model, a maximum likelihood framework is used to register a group of images. To evaluate the performance of the proposed approach, some representative groupwise registration approaches are compared on different image data sets. The experimental results show that the proposed approach has improved performance compared to conventional approaches.

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An Extension of the Standard Mixture Model for Image Segmentation

Cited by 58 publications

References 36 publications

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

Characterization of a robust probabilistic framework for brain magnetic resonance image data distributions

A coarse-to-fine scheme for groupwise registration of multisensor images

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