2018
DOI: 10.1109/access.2018.2873674
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A Glioma Segmentation Method Using CoTraining and Superpixel-Based Spatial and Clinical Constraints

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Cited by 16 publications
(11 citation statements)
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“…Since FCM clustering does not consider the spatial neighborhood information, and the gray distribution of GBM organization will overlap, it is easy to cause misclustering. Algorithms based on graph clustering [12,13] are also very popular now. is type of algorithm uses the vertices of the graph to describe the pixels of the image and uses the edges of the graph to describe the similarity of two pixels, thereby forming a network graph.…”
Section: Related Workmentioning
confidence: 99%
“…Since FCM clustering does not consider the spatial neighborhood information, and the gray distribution of GBM organization will overlap, it is easy to cause misclustering. Algorithms based on graph clustering [12,13] are also very popular now. is type of algorithm uses the vertices of the graph to describe the pixels of the image and uses the edges of the graph to describe the similarity of two pixels, thereby forming a network graph.…”
Section: Related Workmentioning
confidence: 99%
“…Table3: Mean Sensitivity obtained through some segmentation approach Table 3 shows the mean value of Sensitivity that obtained through different segmentation approach on the BraTS datasets, where our OECM approach got 0.98 sensitivity, which is 14% more compare to CTSS [31] approach that, validate the performance of MRI image segmentation.…”
Section: Results and Analysismentioning
confidence: 79%
“…( 2 2)Where, represent for dice score, X represent the extracted region of tumor and Y represent the ground truth. Table 2 shows the mean value of Dice coefficient that obtained through some segmentation approach on the BraTS datasets, where our OECM approach got 0.89 dice score which 1.12% more compare to CTSS [31] approach. Figure 4.4 shows the Box Plot for the Sensitivity; the mean value of sensitivity is obtained as 0.987, which shows the effectiveness segmentation for brain tumor by our proposed approach in MRI images.…”
Section: Results and Analysismentioning
confidence: 99%
“…Yuan et al [15] have discussed about multi-center brain MRI classification by using convolutional neural networks (CNN) approach and obtained 92% of classification accuracy for large MRI dataset. A work of Zhan et al [16] has discussed Glioma segmentation mechanisms by using multiple classifier based collaborative training. Literature has also witnessed usage of feature extraction along with learning approach method for assisting classification of brain as seen in the work of Gumaei et al [17].…”
Section: Introductionmentioning
confidence: 99%