The interpretation of high-resolution computed tomography (HRCT) images of the chest showing disorders of the
lung tissue associated with interstitial lung diseases (ILDs) is time-consuming and requires experience. Whereas
automatic detection and quantification of the lung tissue patterns showed promising results in several studies, its
aid for the clinicians is limited to the challenge of image interpretation, letting the radiologists with the problem
of the final histological diagnosis. Complementary to lung tissue categorization, providing visually similar cases
using content-based image retrieval (CBIR) is in line with the clinical workflow of the radiologists.
In a preliminary study, a Euclidean distance based on volume percentages of five lung tissue types was used
as inter-case distance for CBIR. The latter showed the feasibility of retrieving similar histological diagnoses
of ILD based on visual content, although no localization information was used for CBIR. However, to retrieve
and show similar images with pathology appearing at a particular lung position was not possible. In this work,
a 3D localization system based on lung anatomy is used to localize low-level features used for CBIR. When
compared to our previous study, the introduction of localization features allows improving early precision for
some histological diagnoses, especially when the region of appearance of lung tissue disorders is important.