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Content-Based Image Retrieval System for Differential Diagnosis of Lung Cancer

In this paper the framework for content-based image retrieval (CBIR) system for solid pulmonary nodules from lung CT images has been described. The objective of CBIR system is to retrieve similar nodules from large CT image database for a given query nodule. The CBIR system will work for differential diagnosis of lung cancer as well as radiological self learning tool. In modern hospitals the radiological images and reports are stored in PACS and RIS respectively. Finding similar images based on diagnostic feature will assist radiologists in differential diagnosis during daily clinical practice. Budding radiologists can enrich their perception by visualization of all possible nodules for given query nodule and learn from corresponding reports without assistance of expert radiologists. The proposed standalone CBIR system is validated on 45 solid pulmonary nodules from LIDC dataset 40 solid pulmonary nodules from PGIMER data set respectively. The average precision achieved for LIDC data set is 72.18% and for PGIMER data set is 78.29% considering top five retrieved nodules.