Semantic and Content-Based Medical Image Retrieval with Proven Pathology for Lung Cancer Diagnosis

In lung cancer computer-aided diagnosis (CAD) systems, having an accurate ground truth is critical and time consuming. Due to lack of ground truth and semantic information, lung CAD systems are not progressing in the manner these are supposed to. In this study, we have explored Lung Image Database Consortium (LIDC) database containing annotated pulmonary computed tomography (CT) scans, and we have used semantic and content-based image retrieval (CBIR) approach to exploit the limited amount of diagnostically labeled data in order to annotate unlabeled images with diagnoses. We evaluated the method by various combinations of lung nodule sets as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system Diagnosed dataset and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems while tested on PGIMER data. Also a little knowledge of biopsy confirmed cases can also assist the physician’s as second opinion to mark the undiagnosed cases and avoid unnecessary biopsies