Content Based Image Retrieval Approach in Creating an Effective Feature Index for Lung Nodule Detection with the Inclusion of Expert Knowledge and Proven Pathology

The paper investigates four major issues in the active field of lung computer aided diagnosis (CAD) using content-based image retrieval (CBIR), which are: creating an efficient feature index for lung nodules for similarity measures, database creation of nodules with proven pathology, robust CBIR system and present a self-diagnosing environment to assist the physician in taking the right decision at right time. The results definitely improves the radiologists performance of detecting suspicious nodules based on the ground truth prepared. CBIR has been implemented to expand the small ground truth of 17 nodules to ground truth of 114 nodules based on available biopsy report. Nine out of 83 different extracted features have been considered as the best discriminating features to classify the lung nodules in three classes: Malignant, Benign and Metastasis. LIDC database has been analysed and achieved an average precision of 92.8% , mean average precision (MAP) of 82% at recall 0.1 and an average precision of 88% with PGIMER, Chandigarh. Results in this paper also indicate that the unnecessary biopsies can be avoided as the results are having few number of false positives which can directly increase the specificity of the proposed research.