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.
Aims: This paper presents a computer-aided technique for automatically detecting and segmenting
anomalies in MR images.
Methods: 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.
Results: 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.
Conclusion: Experimental results demonstrated a significantly high accuracy of the BEP-K/R-RX
method in detection of synthetic brain MS lesion data. In the meantime, it also effectively enhances
the detection of brain lesions.