An Adaptive, Knowledge-Driven Medical Image Search Engine for Interactive Diffuse Parenchymal Lung Disease Quantification

Characterization and quantification of the severity of diffuse parenchymal lung diseases (DPLDs) using Computed Tomography (CT) is an important issue in clinical research. Recently, several classification-based computer-aided diagnosis (CAD) systems [1-3] for DPLD have been proposed. For some of those systems, a degradation of performance [2] was reported on unseen data because of considerable inter-patient variances of parenchymal tissue patterns. We believe that a CAD system of real clinical value should be robust to inter-patient variances and be able to classify unseen cases online more effectively. In this work, we have developed a novel adaptive knowledge-driven CT image search engine that combines offline learning aspects of classification-based CAD systems with online learning aspects of content-based image retrieval (CBIR) systems. Our system can seamlessly and adaptively fuse offline accumulated knowledge with online feedback, leading to an improved online performance in detecting DPLD in both accuracy and speed aspects. Our contribution lies in: (1) newly developed 3D texture-based and morphology-based features; (2) a multi-class offline feature selection method; and, (3) a novel image search engine framework for detecting DPLD. Very promising results have been obtained on a small test set.