scholarly journals An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter

2006 ◽  
Author(s):  
John Melonakos ◽  
John Melonakos ◽  
Karthik Krishnan ◽  
Allen Tannenbaum
Keyword(s):  
Image Segmentation ◽  
Prior Knowledge ◽  
Brain Mri ◽  
Image Noise ◽  
Segmentation Algorithm ◽  
Bayes Rule ◽  
Affine Invariant ◽  
Bayesian Segmentation ◽  
Mri Scans ◽  
Anisotropic Smoothing

An Insight Toolkit (ITK) filter for image segmentation with applications to brain MRI scans is presented in this paper. Previously, we showed how ITK could be used to implement our algorithm. This paper presents our new ITK filter for Bayesian segmentation along with results on brain MRI scans. Our algorithm is a refinement of the work of Teo, Saprio, and Wandall. The basic idea is to incorporate prior knowledge into the segmentation through Bayes rule. Image noise is removed via an affine invariant anisotropic smoothing of the posteriors as in Haker et. al. Specifically, we present the implementation of our Bayesian segmentation algorithm applied to brain MRI scans.

scholarly journals Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit

2005 ◽  
Author(s):  
John Melonakos ◽  
John Melonakos ◽  
Ramsey Al-Hakim ◽  
James Fallon ◽  
Allen Tannenbaum
Keyword(s):  
Prior Knowledge ◽  
Brain Mri ◽  
Image Noise ◽  
Segmentation Algorithm ◽  
Affine Invariant ◽  
Knowledge Based ◽  
Mri Scans ◽  
Schizophrenic Patients ◽  
Anisotropic Smoothing

An Insight Toolkit (ITK) implementation of our knowledgebased segmentation algorithm applied to brain MRI scans is presented in this paper. Our algorithm is a refinement of the work of Teo, Saprio, and Wandall. The basic idea is to incorporate prior knowledge into the segmentation through Bayesrule. Image noise is removed via an affine invariant anisotropic smoothing of the posteriors as in Haker et. al. We present the results of this code on two different projects. First, we show the effect of applying this code to skull-removed brain MRI scans. Second, we show the effect of applying this code to the extraction of the DLPFC from a user-defined subregion of brain MRI data.We present our results on brain MRI scans, comparing the results of the knowledge-based segmentation to manual segmentations on datasets of schizophrenic patients.

Optimized Fuzzy Clustering Algorithms for Brain MRI Image Segmentation Based on Local Gaussian Probability and Anisotropic Weight Models

2018 ◽  
Vol 32 (09) ◽  
pp. 1857005 ◽  
Author(s):  
Ting Zhang
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clinical Medicine ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Segmentation Algorithm ◽  
Fuzzy C Means ◽  
Anisotropic Weight ◽  
Mri Image

Brain Magnetic Resonance Imaging (MRI) image segmentation is one of the critical technologies of clinical medicine, and is the basis of three-dimensional reconstruction and downstream analysis between normal tissues and diseased tissues. However, there are various limitations in brain MRI images, such as gray irregularities, noise, and low contrast, reducing the accuracy of the brain MRI images segmentation. In this paper, we propose two optimization solutions for the fuzzy clustering algorithm based on local Gaussian probability fuzzy C-means (LGP-FCM) model and anisotropic weight fuzzy C-means (AW-FCM) model and apply it in brain MRI image segmentation. An FCM clustering algorithm is proposed based on AW-FCM. By introducing the new neighborhood weight calculation method, each point has the weight of anisotropy, effectively overcomes the influence of noise on the image segmentation. In addition, the LGP model is introduced in the objective function of fuzzy clustering, and a fuzzy clustering segmentation algorithm based on LGP-FCM is proposed. A clustering segmentation algorithm of adaptive scale fuzzy LGP model is proposed. The neighborhood scale corresponding to each pixel in the image is automatically estimated, which improves the robustness of the model and achieves the purpose of precise segmentation. Extensive experimental results demonstrate that the proposed LGP-FCM algorithm outperforms comparison algorithms in terms of sensitivity, specificity and accuracy. LGP-FCM can effectively segment the target regions from brain MRI images.

scholarly journals Expression Level of IL-17 in Peripheral Blood of Patients with Late Pregnancy and Diagnosis of Maternal-Fetal Tolerance Based on Brain MRI Image Segmentation Algorithm

2021 ◽  
Vol 37 (6-WIT) ◽  
Author(s):  
Zenying Yu ◽  
Shengyan Zhou ◽  
Zhen Tan ◽  
Guangmin Lu
Keyword(s):  
Image Segmentation ◽  
Peripheral Blood ◽  
Brain Mri ◽  
Late Pregnancy ◽  
Treg Cells ◽  
Severe Preeclampsia ◽  
Segmentation Algorithm ◽  
Reversible Posterior Encephalopathy Syndrome ◽  
Image Segmentation Algorithm ◽  
Mri Image

Objectives: To study the expression of IL-17 in peripheral blood and its effect on maternal-fetal tolerance in patients with eclampsia in late pregnancy using MRI image segmentation algorithm. Methods: Thirty-nine patients with severe preeclampsia and eclampsia with brain symptoms were examined by cranial MRI. Pregnant women with 32 weeks of pregnancy were selected to detect the percentage of Th17 and Treg cells in CD4 + T lymphocytes and the expression of cytokines IL-17 and IL-10 in peripheral blood. Results: MRI examination was normal in 26 cases, 9 cases showed reversible posterior encephalopathy syndrome, three cases were cerebral hemorrhage, and one case was intracranial cavernous sinus thrombosis. two. Compared with the mild preeclampsia group, the relative number of Thl7 cells increased and that of Treg cells decreased in the severe preeclampsia group (P>0.05). Conclusion: The major types of cerebrovascular diseases (CVD) in severe preeclampsia and eclampsia were reversible posterior encephalopathy syndrome and cerebral hemorrhage. It was speculated that the damage to the blood-brain barrier may play an important role in the pathogenesis. The balance of the number of Th17 cells/the number of Treg cells was more inclined to the Th17 cell-mediated pro-inflammatory state, Treg cell-mediated immune tolerance decreases, and it becomes more obvious with the worsening of the disease. doi: https://doi.org/10.12669/pjms.37.6-WIT.4828 How to cite this:Yu Z, Zhou S, Tan Z, Lu G. Expression Level of IL-17 in Peripheral Blood of Patients with Late Pregnancy and Diagnosis of Maternal-Fetal Tolerance Based on Brain MRI Image Segmentation Algorithm. Pak J Med Sci. 2021;37(6):1553-1557.  doi: https://doi.org/10.12669/pjms.37.6-WIT.4828 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

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