The analysis of architectural features in neural tissue sections and the identification of distinct regions is challenging for computer aided diagnosis (CAD) in neuropathology. Due to the difficulty of locating a tissuetextquoterights origin and alignment as well as the vast variety of structures within such images an orientation independent (i. e. rotation invariant) approach for tissue region segmentation has to be found to encode the structural features of neural layer architecture in the tissue. We propose textasciicircum to apply the Ripleytextquoterights-L function, originating from the field of plant ecol- ogy, to compute feature vectors encoding the spatial statistics of point patterns described by selectively stained cells. Combining the Ripleys-L features with unsupervised clustering enables a segmentation of tissue sections into neuropathological areas.