A Novel Pre-processing and Kernel Based Support Vector Machine Classifier with Discriminative Dictionary Learning for Bone Age Assessment

Abstract

Aim of this study: Bone age assessment and X-ray assessment on hand radiographs is an expensive and time consuming process in radiology. This research work presents a powerful Graphical User Interface (GUI) for IRMA that gives exceptional medical image retrieval results. Methods: This study gives complete details regarding the execution of a novel Discriminative Dictionary Learning (DDL) for the purpose of matching query and input medical image samples. In order to further boost the quality of the medical images, Principal Component Analysis (PCA) based noise level estimation scheme is introduced. This helps to eliminate noises included in the medical images. Furthermore, to enhance the performance, a new relevance score scheme via Kernel Support Vector Machine (KSVM) is formulated, which efficiently make use of medical images features in order to discriminate the images accurately. This system initiated a new discriminative name called ‘pairwise similarity measurement’ for the discriminativeness of pairs of query and input medical database images and subsequently integrate it with the classification error for discriminativeness in classifier production to generate a unified objective function. Results: Experimentation results assessed the proposed DDL in IRMA framework on a numerous medical images like BAA images and X-Ray skull images and results confirm that DDL performs far better than the state-of the-art approaches significantly in terms of precision, recall, sensitivity, specificity and accuracy. Conclusion: The resulting DDL has a considerable impact on medical CBIR applications.