A novel image segmentation algorithm based on Hidden Markov Random Field model and Finite Mixture Model parameter estimation

Hu, Kai; Tang, Guangyu; Xiong, Daxi; Qiu, Quan

doi:10.1109/icwapr.2012.6294744

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…But it might fail when vessels are close or their shapes change rapidly [48]. Statistic models [15,19,24] usually describe the data intensity histogram with several distributions (typically Gaussian and Rayleigh), and then estimate the parameters with Expectation Maximization (EM) based algorithm. This kind of approach tend to run into troubles when distributions are overlapped or intra-class variances are large [48].…”

Section: Introductionmentioning

confidence: 99%

Weakly-supervised Cerebrovascular Segmentation Network with Shape Prior and Model Indicator

Chen

Huang

et al. 2022

Proceedings of the 2022 International Conference on Multimedia Retrieval

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Labeling cerebral vessels requires domain knowledge in neurology and could be extremely laborious, and there is a scarcity of public annotated cerebrovascular datasets. Traditional machine learning or statistical models could yield decent results on thick vessels with high contrast while having poor performance on those regions of low contrast. In our work, we employ a statistic model as noisy labels and propose a Transformer-based architecture which utilizes Hessian shape prior as soft supervision. It enhances the learning ability of the network to tubular structures, so that the model can make more accurate predictions on refined cerebrovascular segmentation. Furthermore, to combat the overfitting towards noisy labels as model training, we introduce an effective label extension strategy that only calls for a few manual strokes on one sample. These supplementary labels are not used for supervision but only as an indicator to tell where the model keeps the most generalization capability, so as to further guide the model selection in validation. Our experiments are carried out on a public TOF-MRA dataset from MIDAS data platform, and the results demonstrate that our method shows superior performance on cerebrovascular segmentation which achieves Dice of 0.831±0.040 in the dataset.

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Section: Introductionmentioning

confidence: 99%

Weakly-supervised Cerebrovascular Segmentation Network with Shape Prior and Model Indicator

Chen

Huang

et al. 2022

Proceedings of the 2022 International Conference on Multimedia Retrieval

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“…Ⅱ장에서는 [4] . 그러나 이 min-cut은 같은 영역을 과분할할 수 있다는 단점이 있다 [5] . 이는 한 영역의 크기가 작을 때 두 영역 사이의 에지의 수가 적어지므로 두 영역 간 weight의 합 역시 작아지기 때문이 다.…”

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Image Segmentation using Multi-scale Normalized Cut

Lee¹,

Lee²,

Park³

2013

Journal of Broadcast Engineering

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This paper proposes a fast image segmentation method that gives high segmentation performance as graph-cut based methods. Graph-cut based image segmentation methods show high segmentation performance, however, the computational complexity is high to solve a computationally-intensive eigen-system. This is because solving eigen-system depends on the size of square matrix obtained from similarities between all pairs of pixels in the input image. Therefore, the proposed method uses the small-size square matrix, which is obtained from all the similarities among regions obtained by segmenting locally an image into several regions by graph-based method. Experimental results show that the proposed multi-scale image segmentation method using the algebraic multi-grid shows higher performance than existing methods.

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