Adaptive weighted fuzzy region based optimization for brain MR image segmentation

Srinivasan, A.; Selvarasu, Sadagopan

doi:10.1007/s11042-018-6215-y

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…This method speeds up the detection process without deteriorating the accuracy of fuzzy c-means clustering algorithm. A fuzzy based energy minimization approach has been utilized to detect the tumour region where the membership function is dependent upon the iterative value of the energy (Arulanandam and Selvarasu 2018).…”

Section: Related Workmentioning

confidence: 99%

“…4. Fuzzy c-means clustering based segmentation provides improved accuracy to segment the 2D MR image slices (Arulanandam and Selvarasu 2018;Aslam et al 2015). So, each decision value corresponding to CEFCM segmentation of 2D MR slices are projected into the 3D space for reducing the number of false voxels in the detected complete tumour region.…”

Section: D Reconstruction Phasementioning

confidence: 99%

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Combination of contrast enhanced fuzzy c-means (CEFCM) clustering and pixel based voxel mapping technique (PBVMT) for three dimensional brain tumour detection

Debnath

Talukdar

Islam

2020

J Ambient Intell Human Comput

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Brain tumour is a lump of tissue produced by uncontrolled growth of cells in the human brain. Automated and accurate detection of brain tumour is important for robotics based surgery operation. The proposed method enhances the accuracy, sensitivity and specificity of automated brain tumour detection by adopting an accurate two phase method of detection. The method segments the Axial plane slices of Magnetic Resonance image in its first phase. In the second phase, binary decision values representing the presence or absence of tumor cells on each pixel locations are projected into 3D space to obtain the 3D tumor. Contrast Enhanced fuzzy c-means (CEFCM) clustering method is used to segment the 2D tumor regions from MR image slices due to its high accuracy. The decision values obtained from the segmented image for each pixel locations are mapped into 3D space using Pixel based voxel mapping technique (PBVMT). Average accuracy (Dice overlap coefficient) and average sensitivity of detection are measured with respect to the given ground truth of the image of BRATS 2013 dataset. The overall accuracy, sensitivity and specificity of detection are found to be 0.948, 92.14% and 96.97% respectively.

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Section: Related Workmentioning

confidence: 99%

Section: D Reconstruction Phasementioning

confidence: 99%