Including the perceptual parameter to tune the retrieval ability of pulmonary CBIR systems

The research on Content-Based Image Retrieval (CBIR) is growing in relevance at a fast pace. Algorithms and tools for CBIR can help decision-making processes, for example allowing the specialist to retrieve cases similar to the one under evaluation. However, the main reservation about using CBIR is the semantic gap, which is the divergence among automatic results and what the user is expecting. We propose the ldquoperceptual parameterrdquo, which allows changing the relationship between the feature extraction algorithms and the distance functions, aimed at finding the best integration of both from the specialist\´s point of view. This work integrates the three main elements of similarity queries: the extracted features from the images, the distance function employed to quantify the similarity and the similarity perception from the user. These three elements allowed to build the "similarity operators". The experiments performed show that the new perceptual parameter can narrow the semantic gap between what the system retrieves and what the specialist expects.