Employing Wavelet Transforms to Support Content-Based Retrieval of Medical Images

This paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. We use discrete wavelet transforms to obtain the image representation from a multiresolution point of view. Features of approximation subspaces compose the feature vectors, which succinctly represent the images in the execution of similarity queries. Wavelets and multiresolution method are used to overcome the semantic gap that exists between low level features and the high level user interpretation of images. It also deals with the “ curse of dimensionality”, which involves problems with a similarity definition in high-dimensional feature spaces. This work was evaluated with two different image datasets and the results show an improvement of up to 90% for recall values up to 65%, in the query results using the Daubechies wavelet transform when comparing to other wavelets and gray level histograms.