A low-cost approach for effective shape-based retrieval and classification of medical images

This work aims at developing an efficient support for retrieval and classification of medical images, introducing an approach that comprises techniques of image processing, data mining and fractal theory, leading to an effective and direct way to compare images. A method of feature extraction and comparison is proposed, which uses Zernike moments for invariant pattern recognition as shape features of images' regions of interest. A new 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, another new algorithm, based on fractal theory, is applied to reduce the dimensionality of the representative feature space. In essence, the proposed method determines the smallest set of relevant features that can properly represent images without loss of precision. In addition, the method discards the need of image segmentation, leading to a simple but effective way to make image retrieval by content. Experiments executing k-nearest neighbor queries on medical images reveal that the process is robust and suitable to perform retrieval combined with classification of this kind of images.