Content-based image retrieval system for HRCT lung images: assisting radiologists in self-learning and diagnosis of Interstitial Lung Diseases

Abstract

Content-based Image Retrieval (CBIR) is a technique that can exploit the wealth of the data stored in a repository and help radiologists in decision making by providing references to the image in hand. A CBIR system for High-Resolution Computed Tomography (HRCT) lung images depicting signs of Interstitial Lung Diseases (ILDs) can be built and used as a self-learning tool for budding radiologists. The study of a few lung image retrieval systems available in the literature identifies some important issues that need to be taken care of. In most of the works, the creation of the reference database involves painstaking manual activity, which is time-consuming and needs skilled labor. A lot of human interventions are required, particularly for the proper delineation of the region of interest (ROI) that represents pathology in each of the images in a database. In most cases, the size of the ROIs representing different disease findings are fixed (i.e., either a fixed size square or circle), which at times may not be a proper representation of the disease pattern and as a consequence, it might limit the system’s performance. Until date, a few learning-based approaches have been developed for content-based image retrieval of HRCT lung images, which either learn the similarity using a classifier or get trained through relevance feedback. For medical image analysis, the availability of labelled data for learning makes these learning-based retrieval systems meaningful as it enhances their performance in contrast to their simple distance-based counterpart. The objective of this paper is to develop a CBIR system for ILDs that is reliable and needs minimal human intervention. The paper evaluates the performance of three popular segmentation algorithms. It identifies the best for the effective and automated delineation of an arbitrary region of interest (AROI) depicting the sign of ILDs on HRCT images of the thorax in contrast to the manual delineation of fixed size ROI. This minimizes the manual effort for the creation and maintenance of the reference database, as well as the manual delineation of AROI during query formation. Moreover, AROI created through the automated clustering is found to have a better representation of disease patterns. Three recently proposed general-purpose learning based CBIR techniques are implemented and tested for retrieval of HRCT lung images depicting the sign of ILDs. The best method is suggested after careful evaluation of all the competing techniques.