Using Histogram Skewness and Kurtosis Features for Detection of White Matter Hyperintensities in MRI Images

Abstract

White matter hyperintensities are regions of hyperintensive signal in white brain matter that appear in T2 or FLAIR MRI imaging. They show demyelination, cerebral oedema, tumors, or angiogenesis. Their presence is associated with a number of neurological diseases such as dementia, cognitive impairment, depression, schizophrenia, or even cerebral small vessel disease and multiple sclerosis. WMH areas are thought to appear on MRI images even a few years before the clinical manifestation of certain neurological diseases. Automatic segmentation algorithms can help better understand white matter lesions as part of large-scale studies. It is believed that changes in lesions’ appearance can be monitored over time, thus their correlation with certain diseases can be better understood. This work describes the use of histogram features for the detection of white matter hyperintensities, with a focus on skewness and kurtosis. The first aim of this study is to find features that can be used to identify white matter hyperintensities; then, the authors propose a fully automatic detection algorithm based on a sliding window method to obtain local histograms, and a Support Vector Machine algorithm to perform binary classification. The authors explored the effects of different combinations of characteristics and the relationship between feature values and hyperintensity percentage in a disc-shaped window. The described study is preliminary and contains an initial examination of the effectiveness of using histogram features for the detection of white matter hyperintensities in MRI images.