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Novel Automated Method for the Detection of White Matter Hyperintensities in Brain Multispectral MR Images

Author(s):

Hsian-Min Chen, Clayton Chi-Chang Chen, Hsin Che Wang, Yung-Chieh Chang, Kuan-Jung Pan, Wen-Hsien Chen, Hung-Chieh Chen, Yi-Ying Wu, Jyh-Wen Chai*, Yen-Chieh Ouyang and San-Kan Lee   Pages 1 - 10 ( 10 )

Abstract:


Background: According to the Standards for Reporting Vascular Changes on Neuroimaging, White Matter Hyperintensities (WMHs) are cerebral white matter lesions that are characterized by abnormal tissues of variable sizes and appear hyperintense in T2-weighted Magnetic Resonance (MR) measurements without cavitation (i.e., their tissue signals differ from those of Cerebrospinal Fluid or CSF). Such abnormal tissue regions are typically observed in the MR images of brains of healthy older adults and are associated with a number of geriatric neurodegenerative diseases. Explanations of the exact causes and mechanisms of these diseases remain inconclusive. Moreover, WMHs are typically identified by visual assessment and manual examination, both of which require considerable time. This brings up a need of developing a method for detecting WMHs more objectively and enabling patients to be treated early. As a consequence, damages on nerve cells can be limited and the severity of patients’ conditions can be contained. Discussion: This paper presents a computer-aided technique for automatically detecting and segmenting anomalies in MR images. The method has two steps: (1) a Band Expansion Process (BEP) to expand the dimensions of brain MR images nonlinearly and (2) anomaly detection algorithms to detect WMHs. Synthesized MR images provided by BrainWeb were used as benchmarks against which the detection performance of the algorithms was determined. Conclusion: The most notable findings are as follows. Firstly, compared with the other anomaly detection algorithms and the Lesion Segmentation Tool (LST), BEP-anomaly detection is shown to be the most effective in detecting WMHs. Secondly, across all levels of background noise and inhomogeneity, the mean Similarity Index (SI) produced by our proposed algorithm is higher than that produced by LST, indicating that the algorithm is more effective than LST in segmenting WMHs from brain MR images.

Keywords:

White matter hyperintensities; multispectral MR images; band expansion process (BEP); anomaly detection; RX detector; multiple sclerosis.

Affiliation:

Department of Medical Research, Center for Quantitative Imaging in Medicine (CQUIM), Taichung Veterans General Hospital, Taichung, Taiwan; Department of Biomedical Engineering, Hungkuang University, Taichung, Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Medical Imaging and Radiological Science, Central Taiwan University of Science and Technology, Taichung, Department of Medical Research, Center for Quantitative Imaging in Medicine (CQUIM), Taichung Veterans General Hospital, Taichung, Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan Department of Electrical Engineering, National Chung Hsing University, Taichung, Department of Radiology, Taichung Veterans General Hospital, Taichung, Department of Radiology, Taichung Veterans General Hospital, Taichung, Department of Radiology, Taichung Veterans General Hospital, Taichung, Department of Radiology, Taichung Veterans General Hospital, Taichung, Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan Section of Radiology, College of Medicine, China Medical University, Taichung, Department of Electrical Engineering, National Chung Hsing University, Taichung, Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan Chief Strategy Officer, Tungs`Taichung MetroHarbor Hospital, Taichung



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