The transition from laminar to turbulent flow is a long-standing research subject in the field of fluid mechanics. A basic necessity for such studies is a distinction between laminar and turbulent flow. In particular, a binary segmentation is desired where laminar and turbulent regions behave consistently over time. Previous works in this regard yield inconsistent results, or are restricted to 2D manifolds thereby neglecting the three-dimensional nature of the problem. In this paper, we present a novel use of feature-based methods to segmenting a 3D time-dependent flow into regions of laminar and turbulent behavior. It is based on the extraction of local extrema from a scalar field such as spanwise velocity. It turns out that the existence of many extrema in a region is a good indicator for turbulence. We derive a density function from the extracted extrema using a Kernel Density Estimate (KDE) and threshold it to achieve a binary segmentation into laminar and turbulent regions. We use an in-situ processing approach for data analysis during the simulation run. The two core components of our method exhibit drastically different performance characteristics: the extraction of extrema is embarrassingly parallel, while the KDE is more time-consuming. Hence, we decouple our algorithmic components to achieve a better overall system performance. Our method shows consistent results and enables the domain scientists to study the three-dimensional aspects of the laminar-turbulent transition that were difficult to assess before.