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.
Bronchopulmonary segments are valuable as they give more accurate localization than lung lobes. Traditionally,
determining the segments requires segmentation and identification of segmental bronchi, which, in turn, require
volumetric imaging data. In this paper, we present a method for approximating the bronchopulmonary segments for
sparse data by effectively using an anatomical atlas. The atlas is constructed from a volumetric data and contains
accurate information about bronchopulmonary segments. A new ray-tracing based image registration is used for
transferring the information from the atlas to a query image. Results show that the method is able to approximate the
segments on sparse HRCT data with slice gap up to 25 millimeters.
KEYWORDS: Computer aided diagnosis and therapy, Databases, Lung, Medical imaging, Telemedicine, Java, Computer aided design, Data communications, Knowledge acquisition, Picture Archiving and Communication System
As part of the Learning Medical Imaging Knowledge project, we are developing a knowledge-based, machine learning and knowledge acquisition framework for systematic feature extraction and recognition of a range of lung diseases from High Resolution Computed Tomography (HRCT) images. This framework allows radiologists to remotely diagnose and share expert knowledge about lung HRCT interpretation, which is then used to develop a Computer Aided Diagnosis (CAD) system for lung disease. In this paper, we describe the knowledge acquisition system LMIK, which is Internet-based and platform-independent. The LMIK utilises the Internet to provide users with secure access to patient and research data and facilitates communication among highly qualified radiologists and researchers. It is currently used by five radiologists and over 20 researchers and has proved to be an invaluable research tool. Research is underway to develop computer algorithms for automatic diagnosis of lung diseases. In future, these algorithms will be integrated into LMIK to equip it with CAD capabilities to improve diagnostic accuracy of radiologists and extend availability of expert clinical knowledge to wider communities.