effective shape-based retrieval and classification of mammograms
This paper presents a new approach to support Computer-aided Diagnosis (CAD) aiming at assisting the task of classification and similarity retrieval of mammographic mass lesions, based on shape content. We have tested classical algorithms for automatic segmentation of this kind of image, but usually they are not precise enough to generate accurate contours to allow lesion classification based on shape analyses. Thus, in this work, we have used Zernike moments for invariant pattern recognition within regions of interest (ROIs), without previous segmentation of images. A new data mining algorithm that generates statistical-based association rules is used to identify representative features that discriminate the disease classes of images. In order to minimize the computational effort, an algorithm based on fractal theory is applied to reduce the dimension of feature vectors. K-nearest neighbor retrieval was applied to a database containing images excerpted from previously classified digitalized mammograms presenting breast lesions. The results reveal that our approach allows fast and effective feature extraction and is robust and suitable for analyzing this kind of image.
computer-aided diagnosis zernike moments association rules content-based image retrieval dimensionality reduction similarity measure