Brain Aneurysm Extraction in MRI Images
Pages 286 - 290
Abstract
Medical image processing is the most demanding and emerging field which includes the processing of MRI images. Aneurysm can develop in any blood vessel in the body especially in the brain and abdominal aorta. In regards to brain aneurysm, it has been clarified that the MRI is a more preferable detection method to inspect this brain abnormality. This paper experiments MRI brain aneurysm images using image processing techniques in extracting a clearer image of the brain aneurysm. The method proposed includes preprocessing techniques and post processing techniques. Preprocessing techniques involves noise removal function and image enhancement function. The post processing techniques is segmentation and morphological function. All of these techniques are the basic concepts of image processing. Detection and extraction of brain aneurysm from MRI images of the brain is done by using MATLAB software.
References
[1]
Abood, L.K., 2013. Brain Tumor Extraction in MRI images using Clustering and Morphological Operations Techniques. International Jpurnal of Geographical Information, 4(1), pp. 12--16.
[2]
Garg, R.K., 2017. A Review of Automated MRI Image Processing Techniques Employing Segmentation & Classification. International Journal of Computer Science Trends and Technology (IJCST), 5(2), p. 120.
[3]
Hisanori, H., Yasumi, O., Hiroaki, T. & Yoshinori, O., 2005. Development Of Cerebral Aneurysm Computer-Aided Detection Systems With 3d Mra Data. Yokogawa Technical Report English Edition, I(39), pp. 31--34.
[4]
Hassan, E., 2015. Detecting Brain Tumor from MRI Image using Matlab GUI Program. International Journal of Computer Science and Engineering Survey, 6(6), pp. 47--60.
[5]
Jubin, M., Abhijit C., 2013, Detection of Cerebral Aneurysm by Performing Thresholding-Spatial Filtering-Thresholding Operations on Digital Subtraction Angiogram, Advances in Computing & Information Technology, AISC 177, pp 915--921
[6]
Kamboj, P. & Rani, V., 2013. A Brief Study of Various Noise Model and Filtering Technique. Journal of Global Research in Computer Science, 4(4), pp. 1--6.
[7]
Mittal, K., Shekhar, A., Singh, P. & Kumar, M., 2017. Brain Tumour Extraction using Otsu Based Threshold Segmentation. International Journal of Advanced Research in Computer Science and Software Engineering, 7(4), pp. 159--63.
[8]
Momeni, s., Pourghassem, H., 2015, An Automatic Aneurysm Extraction Algorithm in Fused Brain Digital Subtraction Angiography Images, Biocybernetics and Biomedical Engineering, Vol. 35,no. 4, pp 264--275
[9]
Nassir, S., 2006. Image Segmentation Based on Watershed and Edge Detection Techniques. The International Arab Journal of Information Technology, III(2), pp. 104--10.
[10]
Noriaki, T., Makoto, K., Takahiro, O., et. al., 2015, Bone-free 3D Computed Tomography Angiography Using An Image Processing Ap{plication- Imaging Efficacy Foe Aneurysm Near The Skull Base and Clipped Central Aneurysm, European Neurological Review, 5(1) pp 102--106
[11]
Patil, R.C. & Bhalchandra, A.S., 2014. Brain Tumour Extraction from MRI Images Using Matlab. International Journal of Electronics, Communication & Soft Computing Science and Engineering, 2(1), pp. 1--4.
[12]
Rajapati, S., 2015. Slideshare. {Online} Available at: https://www.slideshare.net/sahilprajapati1/brain-tumor-detection-by-thresholding-approach {Accessed 22 May 2017}.
[13]
Shreeganesh, D., 2012. Slideshare. {Online} Available at: https://www.slideshare.net/DharshikaShreeganesh/brain-tumor-mri-image-segmentation-and-detection-in-image-processing {Accessed 22 May 2017}.
[14]
Yang, X., Blezek, D.J. & Erickson, B.J., 2011. Computer-Aided Detection of Intracranial Aneurysms in MR Angiography. The National Center for Biotechnology Information, I(24), pp. 86--95.
Index Terms
- Brain Aneurysm Extraction in MRI Images
Recommendations
MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique
Medical image processing is the most challenging and emerging field nowadays. Magnetic Resonance Images (MRI) act as the source for the development of classification system. The extraction, identification and segmentation of infected region from ...
Co-registration of in vivo human MRI brain images to postmortem histological microscopic images
Certain features such as small vascular lesions seen in human MRI (Magnetic Resonance Imaging) are detected reliably only in postmortem histological samples by microscopic imaging. Co-registration of these microscopically detected features to their ...
Review of brain MRI image segmentation methods
Brain image segmentation is one of the most important parts of clinical diagnostic tools. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. However, the ...
Comments
Information & Contributors
Information
Published In
February 2019
611 pages
ISBN:9781450365734
DOI:10.1145/3316615
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- University of New Brunswick: University of New Brunswick
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 February 2019
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- LESTARI Grant, Universiti Teknologi MARA
Conference
ICSCA '19
ICSCA '19: 2019 8th International Conference on Software and Computer Applications
February 19 - 21, 2019
Penang, Malaysia
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 100Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 20 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in