Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and
  Healthcare

Medical Image Processing (MIP) is a major ally in e-health and telemedicine, enabling rapid diagnosis with visual, quantitative and analytical assessment. Remote care can reveal subtle changes that indicate the progression of a therapy. Health facilities now have images from plenty of sources which leads to multidimensional images (2D, 3D, 4D, etc.), and multimodality images. For instance, Alzheimer’s disease evaluation still uses behavioral and cognitive tests along with MRI and PET scans of the entire brain. Diverse image collections offer the chance to improve evidence-based diagnosis, administration, teaching, and research. There is a necessity for proper methods to search those collections for images that have similarity in some sense. Statistical bias can be reduced as discoveries are assessed without direct patient contact like quicker and more objective assessment of the effects of anticancer drugs. Content-Based Image Retrieval (CBIR) is an image search framework that complements the usual text-based retrieval of images through visual features, such as color, shape, and texture as search criteria. CBIR can be applied to multidimensional image retrieval, multimodality health data, and the recuperation of unusual datasets.

Abstract

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.

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Most cited references 35

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Content-based image retrieval at the end of the early years

A.W.M. Smeulders, M. Worring, María Santini … (2000)

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Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption.

Paul Tang, Joan S Ash, David Bates … (2005)

Recently there has been a remarkable upsurge in activity surrounding the adoption of personal health record (PHR) systems for patients and consumers. The biomedical literature does not yet adequately describe the potential capabilities and utility of PHR systems. In addition, the lack of a proven business case for widespread deployment hinders PHR adoption. In a 2005 working symposium, the American Medical Informatics Association's College of Medical Informatics discussed the issues surrounding personal health record systems and developed recommendations for PHR-promoting activities. Personal health record systems are more than just static repositories for patient data; they combine data, knowledge, and software tools, which help patients to become active participants in their own care. When PHRs are integrated with electronic health record systems, they provide greater benefits than would stand-alone systems for consumers. This paper summarizes the College Symposium discussions on PHR systems and provides definitions, system characteristics, technical architectures, benefits, barriers to adoption, and strategies for increasing adoption.