Unsupervised vector image segmentation by the ICM method

We propose an unsupervised vector image segmentation technique that combines the Iterated Conditional Modes (ICM) procedure with an initialization scheme that requires minimal prior knowledge. As is well known, every iterative segmentation procedure needs initialization parameters, which are usually obtained from training data. In the absence of such data, the initialization becomes a critical step towards accurate segmentation because bad initializations can lead to poor performance. Our initialization scheme, referred to as Tree Structure (TS) initialization, represents a sequence of binary searches and is similar to a method for data compression in coding theory. The scheme does not require any a priori information or initial parameters, except for the number of classes, and therefore is completely data-driven. Computer simulations on multidimensional magnetic resonance (MR) brain images are provided to demonstrate the overall excellent performance of the proposed TS-ICM method.