Automatic extraction of sulcal lines on cortical surfaces based on anisotropic geodesic distance

Analyzing cortical sulci is important for studying cortical morphology and brain functions. Although sulcal lines on cortical surfaces can be defined in various ways, it is critical in a neuroimaging study to define a sulcal line along the valley of a cortical surface with a high curvature within a sulcus. To extract the sulcal lines automatically, we present a new geometric algorithm based on the computation of anisotropic skeletons of sulcal regions. Because anisotropic skeletons are highly adaptive to the anisotropic nature of the surface shape, the resulting sulcal lines lie accurately on the valleys of the sulcal areas. Our sulcal lines remain unchanged under local shape variabilities in different human brains. Through experiments, we show that the errors of the sulcal lines for both synthetic data and real cortical surfaces were nearly as constant as the function of random noise. By measuring the changes in sulcal shape in Alzheimer's disease (AD) patients, we further investigated the effectiveness of the accuracy of our sulcal lines using a large sample of MRI data. This study involved 70 normal controls (n [men/women]: 29/41, age [mean+/-SD]: 71.7+/-4.9 years), and 100 AD subjects (37/63, 72.3+/-5.5). We observe significantly lower absolute average mean curvature and shallower sulcal depth in AD subjects, where the group difference becomes more significant if we measure the quantities along the sulcal lines rather than over the entire sulcal area. The most remarkable difference in the AD patients was the average sulcal depth (control: 11.70 and AD: 11.34).