The Application of Linear Scale Space and the Spatial Color Model in Microscopy

Grayscale microscopy images acquired with a B/W camera contain structural features for which grayscale scale space provides a robust detection framework. The scale space framework offers robust extraction of features for most computer vision tasks, despite the presence of noise. Feature detectors (2D) can be constructed based on differential invariants, which are relatively insensitive to changes in illumination condition and signal to noise ratio, which is an important feature in light microscopy.

For color light microscopy we use the spatial color model as proposed by Koendering and Geusebroek to select different colored regions and objects. Differential invariants can be connstructed which are insensitive to changes in illumination, color temperature and illumination intensity.

Scalespace and the spatial color model provide the biology microscopist with a powerful and intuititive framework for the detection fo features in microscopy images.