With the aim of reducing the radiologists' subjectivity and the high degree of inter-observer variability, Content-based
Image Retrieval (CBIR) systems have been proposed to provide visual comparisons of a given lesion to a
collection of similar lesions of known pathology. In this paper, we present the effectiveness of shape features versus
texture features for calculating lung nodules' similarity in Computed Tomography (CT) studies. In our study, we used
eighty-five cases of thoracic CT data from the Lung Image Database Consortium (LIDC). To encode the shape
information, we used the eight most commonly used shape features for pulmonary nodule detection and diagnosis by
existent CAD systems. For the texture, we used co-occurrence, Gabor, and Markov features implemented in our previous
CBIR work. Our preliminary results give low overall precision results for shape compared to texture, showing that shape
features are not effective by themselves at capturing all the information we need to compare the lung nodules.
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