Patterns for Next generation Database Systems - A study

In this paper, we propose a novel scheme for efficient content-based
medical image retrieval, dignified according to the Patterns for Next generation
Database systems (PANDA) framework for pattern representation and management.
The proposed scheme use 2-D Wavelet Transform that involves block-based low-level
feature extraction from images. An expectation–maximization algorithm is used to
cluster the feature space to form higher level, semantically meaningful patterns. Then,
the 2-component property of PANDA is exploited: the similarity between two clusters is
estimated as a function of the similarity of both their structures and the measure
components. Experiments were performed on a large set of reference radiographic
images, using different kinds of features to encode the low-level image content. Through
this experimentation, it is shown that the proposed scheme can be efficiently and
effectively applied for medical image retrieval from large databases, providing
unsupervised semantic interpretation of the results, which can be further extended by
knowledge representation methodologies.