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.
Highlights
At present, direct digital imaging methods are of growing significance in medical diagnosis
In the architecture of Content Based Image Retrieval (CBIR) system for the purpose of medical applications, initially Principal Component Analysis (PCA) based technique is employed to eliminate noise from medical images, in view of the fact that the noise exists in the medical images samples which considerably diminishes the clarity, excellence of the image
The objective function for learning a pairwise constrained dictionaryfor query and input image feature vectors from Kernel Support Vector Machine (KSVM) with both reconstructive and discriminative power can be given as: Image Retrieval with Medical Image (IRMA) framework
Summary
Direct digital imaging methods are of growing significance in medical diagnosis. The complication of archiving those medical image sets have been handled with numerous solutions like Picture Archiving and Communication Systems (PACS) (Gutierrez et al, 2006) or modular and dedicated systems for the utilization image databases (Marcos et al, 2007). Efficiency of those systems may perhaps be crucial in medical practice, in view of the fact that they are accountable for accumulating medical images in a responsible manner. These systems have to permit users to resourcefully access this information
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