Radiological image classification using HMMs and Shape contexts

This paper presents a new system for radiological image classification. The proposed system is built on Hidden Markov Models (HMMs). In this work, the Hidden Markov Models Toolkit (HTK) is adapted to deal with image classification issue. HTK was primarily designed for speech recognition research. Features are extracted through Shape context descriptor. They are converted to HTK format by first adding headers, then, representing them in successive frames. Each frame is multiplied by a windowing function. Features are used by HTK for training and classification. Classes of the medical IRMA database are used in experiments. A comparison with a neural network based system shows the efficiency of the proposed approach.