A Glioma Segmentation Method Using CoTraining and Superpixel-Based Spatial and Clinical Constraints

Zhan, Tianming; Shen, Fangqing; Hong, Xiangfei; Wang, Xihu; Chen, Yunjie; Lu, Zhenyu; Yang, Guowei

doi:10.1109/access.2018.2873674

Cited by 16 publications

(11 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Zhang

Zhou

Liao

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

In order to improve the accuracy of glioma segmentation, a multimodal MRI glioma segmentation algorithm based on superpixels is proposed. Aiming at the current unsupervised feature extraction methods in MRI brain tumor segmentation that cannot adapt to the differences in brain tumor images, an MRI brain tumor segmentation method based on multimodal 3D convolutional neural networks (CNNs) feature extraction is proposed. First, the multimodal MRI is oversegmented into a series of superpixels that are uniform, compact, and exactly fit the image boundary. Then, a dynamic region merging algorithm based on sequential probability ratio hypothesis testing is applied to gradually merge the generated superpixels to form dozens of statistically significant regions. Finally, these regions are postprocessed to obtain the segmentation results of each organization of GBM. Combine 2D multimodal MRI images into 3D original features and extract features through 3D-CNNs, which is more conducive to extracting the difference information between the modalities, removing redundant interference information between the modalities, and reducing the original features at the same time. The size of the neighborhood can adapt to the difference of tumor size in different image layers of the same patient and further improve the segmentation accuracy of MRI brain tumors. The experimental results prove that it can adapt to the differences and variability between the modalities of different patients to improve the segmentation accuracy of brain tumors.

show abstract

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Zhang

Zhou

Liao

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…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%

Tumor Segmentation using Optimize Evidential C-Means at Brain MRI Images

Sangolgi*,

Sasikala

2020

IJITEE

View full text Add to dashboard Cite

The accurate treatment of tumor is the major key for diagnosis and therapy, so the development in an area of image processing provide greater contribution in order to detect the tumors in human brain. A medical imaging technique such as MRI is generally used to capture the human brain images. In this paper, we addressed a PbET that is very effective process for reasoning and modelling with the presence of imprecise information and uncertainty. In the PbET function, we will propose an Optimize Evidential C-Means (OECM) approach for the delineation of Gliomas tumor in a MRI brain images. An OECM approach is integrated with spatial regularization and LM for the tumor segmentation in MRI brain image, where the LM is consider to measure the distance for better representation of comparisons between surrounding voxels and the clustering distortion. In order to validate our proposed model, we compared with different brain tumor segmented approach in terms of dice coefficient and sensitivity

show abstract

“…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%

An optimized approach for extensive segmentation and classification of brain MRI

Harish¹,

Ahammed²

2020

IJECE

View full text Add to dashboard Cite

With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.

show abstract

A Glioma Segmentation Method Using CoTraining and Superpixel-Based Spatial and Clinical Constraints

Cited by 16 publications

References 26 publications

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images

Tumor Segmentation using Optimize Evidential C-Means at Brain MRI Images

An optimized approach for extensive segmentation and classification of brain MRI

Contact Info

Product

Resources

About