An Improved Method for Automatic Segmentation and Accurate Detection of Brain Tumor in Multimodal MRI

Bhima, K.; Jagan, A

doi:10.5815/ijigsp.2017.05.01

Cited by 12 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Comparison of the various state-of-the-art automatic and semi automatic segmentation methods for brain MR Images are presented [1][4] [13].…”

Section: Fig1 3d Multimodal Brain Mr Imagesmentioning

confidence: 99%

See 1 more Smart Citation

Robust and Accurate Automated Methods for Detection and Segmentation of Brain Tumor in MRI

Bhima*,

Jagan

2019

IJRTE

View full text Add to dashboard Cite

In this proposed study, a novel Multimodal brain MR image segmentation method is presented to overcome the unattractive and undesirable over segmentation characteristics of conventional Watershed method. The proposed work, presents Optimal Region Amalgamation Technique (RAT) that merge the Watershed method (spatial domain) and Fuzzy C-means clustering (feature spaces) to reduce the unattractive and undesirable over segmentation in brain MR images. In the proposed work, to improve the quality of segmentation results of Watershed method, initially it construct a RAG(Region Merging Graph) for optimal RAT by applying the most popular MRF(Markov Random Field) method . Consequently, the inter-region comparison is presented by applying the watershed method in Spatial Domain and Fuzzy C-Means clustering method in Feature Space for image mapping to compute the Optimal Region Amalgamation. Further, to determine the Feature space and domain space illustration of the brain MR image segmentation, the SGD (Spatial Graph Depiction) is presented that is computed with FSD (Feature Space Depiction) which is obtained by watershed partitioning and FCM clustering method. The experimental results on multimodal brain MR image datasets presents that the proposed novel Optimal Region Amalgamation Technique (RAT) exhibits more promising MR images segmentation results with compared to the traditional watershed method. Finally, an assessment and evaluation of the state-of-the-art brain tumor segmentation methods are presented and future directions to improve and standardize the detection and segmentation of brain tumor into daily clinical treatment are addressed.

show abstract

“…The Comparison of the various state-of-the-art automatic and semi automatic segmentation methods for brain MR Images are presented [1][4] [13].…”

Section: Fig1 3d Multimodal Brain Mr Imagesmentioning

confidence: 99%

“…Proposed work permits for the in details analysis and diagnosis of brain tumor in 3D Multimodal Brain MR Images. [13] is most popular brain MR image segmentation technique for brain tumor. FCMC is unsupervised automatic brain tumor segmentation methods as shown in [ Figure- (1) and Equation (2).…”

mentioning

confidence: 99%

Robust and Accurate Automated Methods for Detection and Segmentation of Brain Tumor in MRI

Bhima*,

Jagan

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…volumes, the proposed method yields 3 to 4 times faster results and higher Dice value than traditional K-Means method. The research work [17] deals with the accurate segmentation and detection of tumors in multimodal brain MRI, and this work is focused to improve automatic segmentation results. This work analyses the segmentation performance of existing state-of-art method on improved Fuzzy C-Means Clustering.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Modeling and Correlation Analysis of Brain Plasticity Mechanisms in Stroke Patients

I.V.¹,

Mayorova²,

V.V.³

et al. 2019

IJISA

View full text Add to dashboard Cite

The aim of this research is the study of pathogenic signs, prognostically significant for the outcome of the disease and restoration of impaired functions at various stages of recovery after a stroke. This work describes a new method of applying a group of artificial neural network algorithms for each of the criteria and for each period of rehabilitation, and it is aimed at analyzing the structural and functional support of motor and higher cognitive functions, including speech and language as well as brain plasticity after ischemic stroke. The functional magnetic resonance imaging (fMRI, DTI) and clinical data machine learning algorithms were used. Self-organizing Kohonen and probabilistic neural network-based models with different structures and parameters were developed and applied for each criterion for periods of 3, 6, and 12 months of rehabilitation. For correlation analyses and modeling additional classifiers, we used: Decision Tree (DT), Support Vector Machine (SUM), k-Nearest Neighbor (KNN) clustering, and Logistic Regression (LR). In the performance evaluation, sensitivity, specificity, accuracy, error rate, and f-measure were used. The using of clinical parameters and mathematical modeling for analysis of brain plasticity mechanisms in stroke patients allowed in some cases to predict cognitive functions within the accuracy of 85-97%. Moreover, it is shown that the functional systems is represented by various brain structures, its synchronous activity and structural connectivity ensures the rapid and most complete restoration of motor and higher cognitive functions, including speech and language (effective post-stroke plasticity of the brain) after a course of neurorehabilitation.

show abstract

“…One of the fundamental processes in the chain of image processing [5] is the segmentation. The segmentation is a difficult problem [ The method of k-means [7] has been very used in several application and field of research, on the one hand for its simplicity of implementation and on the other hand because it can provide a good approximation of the segmentation sought.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Abnormal Blood Cells for Biomedical Diagnostic Aid

Bouzid-Daho¹,

Boughazi²

2018

IJIGSP

View full text Add to dashboard Cite

The aim of our work is to obtain a maximum rate of recognition of abnormal (cancerous) blood cells. We propose the development of a system based on kmeans methods, after an RGB channel decomposition by applying the algorithm which can segment our microscopic medical images. It turns out that the proposed system shows better segmentation and classification for the identification and detection of leukemia. The experimental results obtained are very encouraging, which helps hematologists to monitor the evolution of cancerous blood cells and make a good diagnosis.

show abstract

An Improved Method for Automatic Segmentation and Accurate Detection of Brain Tumor in Multimodal MRI

Abstract: Abstract-Automatic

Cited by 12 publications

References 7 publications

Robust and Accurate Automated Methods for Detection and Segmentation of Brain Tumor in MRI

Robust and Accurate Automated Methods for Detection and Segmentation of Brain Tumor in MRI

Neural Network Modeling and Correlation Analysis of Brain Plasticity Mechanisms in Stroke Patients

Segmentation of Abnormal Blood Cells for Biomedical Diagnostic Aid

Contact Info

Product

Resources

About