2014 2nd International Conference on Emerging Technology Trends in Electronics, Communication and Networking 2014
DOI: 10.1109/et2ecn.2014.7044982
|View full text |Cite
|
Sign up to set email alerts
|

Brain tumor extraction from MRI image using mathematical morphological reconstruction

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
9
0

Year Published

2015
2015
2023
2023

Publication Types

Select...
6
3
1

Relationship

1
9

Authors

Journals

citations
Cited by 17 publications
(9 citation statements)
references
References 4 publications
0
9
0
Order By: Relevance
“…The mentioned target can be artificial (for example, circular) or natural feature which is located within the defined effective area or region of interest (ROI). In the next stage, by filling the holes in the image and removing noises, the target is detected [ 47 ]. Finally, the 2D center position of each target is determined by the image analysis algorithm of region descriptor [ 48 ].…”
Section: Methodology Of Deformation Measurementmentioning
confidence: 99%
“…The mentioned target can be artificial (for example, circular) or natural feature which is located within the defined effective area or region of interest (ROI). In the next stage, by filling the holes in the image and removing noises, the target is detected [ 47 ]. Finally, the 2D center position of each target is determined by the image analysis algorithm of region descriptor [ 48 ].…”
Section: Methodology Of Deformation Measurementmentioning
confidence: 99%
“…They exploit underlying differences in intensity values between normal and abnormal regions. This encompasses watershed segmentation [28] followed by the application of some morphological operations to detect tumor regions in an MRI slice [8,29]. However image processing based approaches often suffer from severe over-segmentation and noise, in the form of false positive regions, resulting in inappropriately delineated tumor region.…”
Section: Overview Of Brain Tumor Detectionmentioning
confidence: 99%
“…Megersa and Alemu [22] have designed an automated framework based on neural network composed of preprocessing, image fusion, classification task for automatic image segmentation and tumor detection form brain MRI. In [23] In the same way Senthilkumaran and Thimmiaraja [26] have used Histogram equalization and Sharma and Meghrajani [27] have used mathematical morphological model to enhance the medical image visibility for efficiently analyzing barin tumor from MRI. The work of Sulaiman et al [28] presented a new model based on the clustering algorithm, which objective is to remove the salt and pepper niose from the brain MRI.…”
Section: Introductionmentioning
confidence: 99%