Efficient analysis of medical images to assist physician’s decision making is an important task. However, the analysis of such images often requires sophisticated segmentation and classification algorithms. An approach to speed up these time consuming operations is to use parallel processing. In this paper a new parallel system for medical image analysis is presented. The system combines distributed and shared memory architectures using MPI and the inter-process communication switching mechanism (IPC). MPI is used to communicate between nodes and shared-memory IPC is used to perform shared memory operations among processors within a node. We show how to map a clinical endoscopic image analysis algorithm efficiently onto this architecture. This results in an implementation with significant runtime savings.