As soon as it was possible to scan and load medical images into a computer, researchers have attempted to built system to automate the analysis of such images. Initially, from 1970s to 1990s, medical image analysis was done using sequential application of low level pixel processing(edge and line detector filters) and mathematical modeling to construct a rule-based system that could solve only particular task. At the same time there were some agents based on if-else rules, popular in field of Artificial Intelligence commonly known as GOFAI (Good Old Fashioned Artificial Intelligence) agent. Towards the end of 1990s, supervised techniques were becoming popular in which training data was used to train models and they were becoming increasingly popular in the field of medical image analysis. Examples may include active shape model, atlas method.

We investigate the computational properties of the spatial algebra RCC-5 which is a restricted version of the RCC framework for spatial reasoning. The satisfiability problem for RCC-5 is known to be NP-complete but not much is known about its approximately four billion subclasses. We provide a complete classification of satisfiability for all these subclasses into polynomial and NP-complete respectively. In the process, we identify all maximal tractable subalgebras which are four in total.