A survey on detection of brain tumor from MRI brain images

Aswathy, S. U.; Dhas, Gnana; Kumar, S. S.

doi:10.1109/iccicct.2014.6993081

Cited by 41 publications

(27 citation statements)

References 12 publications

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“…Parametric and Geometric deformable models combine the prior knowledge related to the location, size and shape of different structures with the constraints learnt from the image data to deform the original model that then produces the segmented model of the brain MRI [39].K-Nearest Neighbors method can be used to segment the MR image by classifying each pixel using an integer value k that determines the number of neighboring pixels to be examined, a Euclidean distance to determine the k closest neighbors and a set of labeled examples for some training data [41]. ISSN …”

Section: Segmentationmentioning

confidence: 99%

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A Survey of MRI Segmentation Techniques for Brain Tumor Studies

Udupa¹,

Vishwakarma²

2016

BIJAIP

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Section: Segmentationmentioning

confidence: 99%

“…Repeated iterations to improve the classification result can make the method slow. It is not favorable for MRI tumor segmentation when the training set has some of the underrepresented classes [41].…”

Section: Segmentationmentioning

confidence: 99%

A Survey of MRI Segmentation Techniques for Brain Tumor Studies

Udupa¹,

Vishwakarma²

2016

BIJAIP

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“…It is observed that the experimental results of the proposed method gives better result in comparison to other techniques. Aswathy, S. U. et al [12] have discussed that the Brain tumor detection plus segmentation is one of the most challenging and very time consuming task in medical processing. MRI has a medical technique, mainly utilized by the radiologist for visualization of internal structure of the human body without surgery.…”

Section: Literature Surveymentioning

confidence: 99%

Improvement of Brain Tumor Feature based Segmentation using Decision based Alpha Trimmed Global Mean Filter

Sharma¹,

Singh²

2015

IJCA

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Detection of the brain tumor is an important application of medical image processing. The literature survey in this paper has shown that the many of the existing methods has unobserved the deprived quality images like images with amount of noise or poor brightness. Moreover the much of the existing work on tumor detection has abandoned the use of object based segmentation. The overall goal of this research work is to propose an efficient brain tumor detection using the feature detection androundness metric. To enhance the tumor detection rate further we have integrated the proposed object based tumor detection with the Decision based alpha trimmed global mean. The proposed technique has the ability to produce effective results even in case of high density of the noise.

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“…Classification methods consist of two categories [32]: supervised classification and unsupervised classification. Both achieved satisfactory results; however, supervised classification performed better than unsupervised classification in terms of classification accuracy (successful classification rate) [33].…”

Section: Feed-forward Neural Networkmentioning

confidence: 99%

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

Zhang¹,

Wang²,

Dong³

et al. 2015

PIER

109

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Abstract-(Background)We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.

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A survey on detection of brain tumor from MRI brain images

Cited by 41 publications

References 12 publications

A Survey of MRI Segmentation Techniques for Brain Tumor Studies

A Survey of MRI Segmentation Techniques for Brain Tumor Studies

Improvement of Brain Tumor Feature based Segmentation using Decision based Alpha Trimmed Global Mean Filter

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

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