  Brightfield and fluorescent imaging of whole brain sections are funda- mental
tools of research in mouse brain study. As sectioning and imaging become more
efficient, there is an increasing need to automate the post-processing of sec-
tions for alignment and three dimensional visualization. There is a further
need to facilitate the development of a digital atlas, i.e. a brain-wide map
annotated with cell type and tract tracing data, which would allow the
automatic registra- tion of images stacks to a common coordinate system.
Currently, registration of slices requires manual identification of landmarks.
In this work we describe the first steps in developing a semi-automated system
to construct a histology at- las of mouse brainstem that combines atlas-guided
annotation, landmark-based registration and atlas generation in an iterative
framework. We describe an unsu- pervised approach for identifying and matching
region and boundary landmarks, based on modelling texture. Experiments show
that the detected landmarks corre- spond well with brain structures, and
matching is robust under distortion. These results will serve as the basis for
registration and atlas building.
