Image-based discriminating morphological features for gastric atrophy assessment: a step to go further.

The aim of this study is to establish a basis for automated assessment of gastric atrophy according to the Updated Sydney System. We sought to minimize inter- and intra-observer variations in the application of the Sydney System. A total of 160 biopsies were examined by three pathologists and graded using the visual scale of the Updated Sydney System. A consensus was reached on 135 biopsies. Digital images were captured for the studied biopsies. Image processing techniques were used to extract four morphological features that uniquely discriminate each atrophy grade. The features are related to gland density and shape. To validate the reproducibility of these features, the K-Means clustering technique was used. We were able to grade the atrophy with an overall precision of 95.6%. Furthermore, the proposed features were able to distinguish four discrete grades without any significant overlap. This has not been achieved by previous studies.