Towards Improved Epilepsia Diagnosis by Unsupervised Segmentation of Neuropathology Tissue Sections using Ripley’s- $${\rm{\hat L}}$$ Features

Abstract

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 tissue’s 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 to apply the Ripley’s-\(\hat L\) function, originating from the field of plant ecology, to compute feature vectors encoding the spatial statistics of point patterns described by selectively stained cells. Combining the Ripley’s \(\hat L\) features with unsupervised clustering enables a segmentation of tissue sections into neuropathological areas.