An automated multimodal white matter hyperintensity identification for MRI images using image processing

• Published in: 2017 International Conference on Electrical, Electronics and System Engineering, ICEESE 2017
• Main Author: Isa I.; Sulaiman S.N.; Md. Tahir N.; Abdullah M.F.; Che Soh Z.H.; Mustapha M.; Karim N.K.A.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2017
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050494648&doi=10.1109%2fICEESE.2017.8298386&partnerID=40&md5=350129d32821ca4ca036832fcfe376fd

White matter hyperintensities (WMH) are small regions of high signal intensity that are observable on the white matter region of the brain through magnetic resonance imaging images. Generally, the medical expert conducts a white matter hyperintensities analysis to investigate brain tissue abnormality using manual or semi-automatic methods. However, those methods are prone to error and they establish unreliable results as different in rating scales. In this paper, a fully automatic method is proposed to identify WMH using the multimodal technique which combining image segmentation and enhancement. This method is introduced as an unsupervised method to automatically segment WMH on MRI images of T2-weighted and FLAIR sequences. Subsequently, the processed sequences are integrated by overlying the mapping images in order to map the most precise WMH regions. The accuracy of the WMH regions identification is assessed through the similarity index between automated and manual approach. The experimental results show that the proposed method has achieved significant results to detect exact WMH area. The proposed method is suitable to be implemented in analyzing white matter hyperintensities identification and it may serves as a computer-aided tool for radiologists. © 2017 IEEE.