Abstract
Image processing techniques have been important and widely used for image analysis due to the advancement of computer learning. Analyzing of processed image is done to obtain any quantitative information or data from the processed images. Hence, segmentation is a part of image processing that has various types of techniques and algorithms to be used. Generally, the main purpose of the segmentation is to improve or potentially change the image digitally so that any useful information is easier to analyze. However, the effectiveness of segmentation algorithms evaluation has become the priority to get results through the process. Therefore, this study is used to make comparisons of different image segmentation techniques based on MRI images. Different clustering algorithms are tested on MRI images to identify white matter hyperintensities (WMH) region on human brain. The accuracy of the identification assessment is compared between several MRI image segmentation techniques. The best performance analysis is suitable to be implemented on any computer-aided tool for medical monitoring or analyzing purpose.
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Acknowledgements
The authors highly show gratitude to Advanced Medical and Dental Institute (AMDI), Kepala Batas, Universiti Sains Malaysia, and special thanks to Faculty of Electrical Engineering, Universiti Teknologi MARA P. Pinang. Special appreciation to the Human Research Ethics Committee of USM (JEPeM) for approval under code USM/JEPeM/16090293. Also special thanks to research interest group of Advanced Rehabilitation Engineering in Diagnostic and Monitoring Research Group (AREDiM).
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Afandi, A., Isa, I.S., Sulaiman, S.N., Marzuki, N.N.M., Karim, N.K.A. (2020). Comparison of Different Image Segmentation Techniques on MRI Image. In: Zhang, YD., Mandal, J., So-In, C., Thakur, N. (eds) Smart Trends in Computing and Communications. Smart Innovation, Systems and Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-0077-0_1
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DOI: https://doi.org/10.1007/978-981-15-0077-0_1
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