Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images
Gajanayake, Randike, Yapa, Roshan Dharshana, & Hewavithana, Badra (2009) Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images. In Proceedings of the 4th International Conference on Industrial and Information Systems, ICIIS 2009, IEEE, University of Peradeniya, Sri Lanka, pp. 301-305.
In the analysis of medical images for computer-aided diagnosis and therapy, segmentation is often required as a preliminary step. Medical image segmentation is a complex and challenging task due to the complex nature of the images. The brain has a particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues in order to prescribe appropriate therapy. Magnetic Resonance Imaging is an important diagnostic imaging technique utilized for early detection of abnormal changes in tissues and organs.
It possesses good contrast resolution for different tissues and is, thus, preferred over Computerized Tomography for brain study. Therefore, the majority of research in medical image segmentation concerns MR images. As the core juncture of this research a set of MR images have been segmented using standard image segmentation techniques to isolate a brain tumor from the other regions of the brain.
Subsequently the resultant images from the different segmentation techniques were compared with each other and analyzed by professional radiologists to find the segmentation technique which is the most accurate.
Experimental results show that the Otsu’s thresholding method is the most suitable image segmentation method to segment a brain tumor from a Magnetic Resonance Image.
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|Item Type:||Conference Paper|
|Additional Information:||INSPEC Accession Number: 11179385|
|Keywords:||Image Processing, segmentation, MRI, Brain, Tumor, medical image processing|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)|
|Divisions:||Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Institutes > Information Security Institute
|Copyright Owner:||Copyright 2009 IEEE|
|Copyright Statement:||Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||23 Jun 2011 23:06|
|Last Modified:||16 Aug 2011 09:40|
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