LMIK - learning medical image knowledge: an Internet-based medical image knowledge acquisition framework
| Autor: | Peter Wilson, Phil Lucas, Arcot Sowmya, James S. J. Wong, Mamatha Rudrapatna, George Kossoff, Sata Busayarat, Avishkar Misra, Tatjana Zrimec |
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| Rok vydání: | 2003 |
| Předmět: |
Telemedicine
business.industry Computer science education Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Data science Knowledge acquisition ComputingMethodologies_PATTERNRECOGNITION Knowledge base Computer-aided diagnosis Medical imaging The Internet Computer vision Artificial intelligence business |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.526290 |
| Popis: | As part of the Learning Medical Imaging Knowledge project, we are developing a knowledge-based, machine learning and knowledge acquisition framework for systematic feature extraction and recognition of a range of lung diseases from High Resolution Computed Tomography (HRCT) images. This framework allows radiologists to remotely diagnose and share expert knowledge about lung HRCT interpretation, which is then used to develop a Computer Aided Diagnosis (CAD) system for lung disease. In this paper, we describe the knowledge acquisition system LMIK, which is Internet-based and platform-independent. The LMIK utilises the Internet to provide users with secure access to patient and research data and facilitates communication among highly qualified radiologists and researchers. It is currently used by five radiologists and over 20 researchers and has proved to be an invaluable research tool. Research is underway to develop computer algorithms for automatic diagnosis of lung diseases. In future, these algorithms will be integrated into LMIK to equip it with CAD capabilities to improve diagnostic accuracy of radiologists and extend availability of expert clinical knowledge to wider communities. |
| Databáze: | OpenAIRE |
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