KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Brain MRI Template-Driven Medical Images Mapping Method Based on Semantic Features for Ischemic Stroke

허혈성 뇌졸중을 위한 뇌 자기공명영상의 의미적 특징 기반 템플릿 중심 의료 영상 매핑 기법

  • Received : 2016.01.18
  • Accepted : 2016.02.01
  • Published : 2016.02.29
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Abstract

Ischemic stroke is a disease that the brain tissues cannot function by reducing blood flow due to thrombosis or embolisms. Due to the nature of the disease, it is most important to identify the status of cerebral vessel and the medical images are necessarily used for its diagnosis. Among many indicators, brain MRI is most widely utilized because experts can effectively obtain the semantic information such as cerebral anatomy aiding the diagnosis with it. However, in case of emergency diseases like ischemic stroke, even though a intelligent system is required for supporting the prompt diagnosis and treatment, the current systems have some difficulties to provide the information of medical images intuitively. In other words, as the current systems have managed the medical images based on the basic meta-data such as image name, ID and so on, they cannot consider semantic information inherent in medical images. Therefore, in this paper, to provide core information like cerebral anatomy contained in brain MRI, we suggest a template-driven medical images mapping method. The key idea of the method is defining the mapping characteristics between anatomic feature and representative images by using template images that can be representative of the whole brain MRI image set and revealing the semantic relations that only medical experts can check between images. With our method, it will be possible to manage the medical images based on semantic.

허혈성 뇌졸중은 뇌혈관의 혈전이나 색전에 의해 뇌 혈류가 감소하게 되어 뇌 조직이 기능을 못하는 질환으로, 질환의 특성상 뇌혈관의 폐색 여부를 확인하는 것이 중요하기 때문에 질환의 진단에 있어서 의료 영상이 필수적으로 활용된다. 그 중에서도 뇌 자기공명영상은 뇌의 구조적인 정보들을 얻을 수 있어 질환을 진단하는데 그 지표로 널리 활용되고 있다. 하지만 허혈성 뇌졸중과 같은 응급 질환의 경우 빠른 진단과 처치에 도움이 될 수 있는 지능적인 시스템이 요구됨에 비해, 기존의 의료 영상 저장 시스템으로는 신속하고 직관적인 영상 정보 제공이 어렵다. 즉, 기존의 시스템은 피상적인 메타 데이터를 이용하여 의료 영상을 관리하고 있어 의료 영상에 내재된 주요 의미적 정보를 고려하지 못하고 있다. 따라서 본 논문에서는 뇌 자기공명영상이 내포하고 있는 주요 의미적인 정보인 뇌의 해부학적 구조와 같은 영상 정보를 제공할 수 있도록 하는 템플릿 중심의 영상 매핑 기법을 제안하고자 한다. 제안하는 기법은 방대한 양의 영상을 대표할 수 있는 대표 영상(템플릿)을 선정하여 의미적 특징과 대표 영상(템플릿) 사이의 대응성을 정립하고, 전문가(의사)에 의해서만 분석될 수 있는 영상 사이의 의미적 연관성을 표면화 시켜 의미 기반의 영상 관리를 가능케 한다.

Keywords

References

  1. S. U. Kwon, "Treatment of acute ischemic stroke: Thrombolysis," Korean Journal of Cerebrovasular Disease, Vol.3, No.2, pp.113-118, 2001.
  2. K. H. Baek, "Analysis and consideration of the cases related to emergency medical care - Focusing on the three major emergent diseases severe acute," Korean Journal of Medicine and Law, Vol.22, No.2, pp.57-86, 2013.
  3. H. J. Kim and C. L. Lee, "PACS and medical imaging display systems," Journal of the Korean Society for Precision Engineering, Vol.25, No.1, pp.22-34, 2008.
  4. J. S. Kim and M. S. Cho, "Introduction and application for cardiocerebralvascular center," Korean Journal of Digital Imaging Medical, Vol.12, No.1, pp.9-14, 2010.
  5. F. Valente, C. Viana-Ferreira, C. Coasta, and J. L. Oliveira, "A RESTful image gateway for multiple medical image repositories," IEEE Transaction: Information Technology in Biomedicine, Vol.16, No.3, pp.356-364, 2012. https://doi.org/10.1109/TITB.2011.2176497
  6. "DICOM RIS/PACS telemedicine network implementation using free open source software," Latin America Transaction, IEEE, Vol.11, No.1, pp.168-171, 2013. https://doi.org/10.1109/TLA.2013.6502797
  7. H. Greenspan and A. T. Pinhas, "Medical image categorization and retrieval for PACS using the GMM-KL framework," IEEE Transactions: Information Technology in Biomedicine, Vol.11, No.2, pp.190-202, 2007. https://doi.org/10.1109/TITB.2006.874191
  8. Y. Tao, Z. Peng, A.Krishman, and X. S. Zhou, "Robust learning-based parsing and annotation of medical radiographs," IEEE Transactions: Medical Imaging, Vol.30, No.2, pp.338-350, 2011. https://doi.org/10.1109/TMI.2010.2077740
  9. The Log Metadata Element of PACS standard (TTA standard). TTAK.OT-10.0245.
  10. S. Gupta, A. Mishara, and R. Menaka, "Ischemic stroke detection using image processing and ANN," in Proceeding of the International Conference on Advanced in Computing, Communications and Information, Ramanathapuran, pp.1416-1420, 2015.
  11. P. R. Mirajkar, A. Singh, K. A. Bhagwat, and M. E. Ashalatha, "Acute ischemic stroke detection using wavelet based fusion of CT and MRI image," in Proceeding of the International Conference on Advanced in Computing, Communications and Information, Kochi, pp.1123-1130, 2015.
  12. G. T. Oh, H. B. Jang, U. J. Do, and H. Y. Lee, "Multimedia processing: Reversible watermarking based on predicted error histogram for medical imagery," KIPS Transaction on Software and Data Engineering, Vol.4, No.5, pp.231-240, 2015. https://doi.org/10.3745/KTSDE.2015.4.5.231
  13. H. J. Kim and S. H. Gwak, "A parametric image enhancement technique for contrast-enhanced ultrasonography," KIPS Transaction on Software and Data Engineering, Vol.3, No.6, pp.231-236, 2014. https://doi.org/10.3745/KTSDE.2014.3.6.231
  14. I. S. Cho and H. S. Kwon, "Efficient sharing system of medical information for interoperability between PACS system," Journal of the Korea Institute of Information and Communication Engineering, Vol.13, No.3, pp.498-504, 2009.
  15. Peter D. Schellinger, et al, "A standardized MRI stroke protocol comparison with CT in hyperacute intracerebral hemorrhage," American Heart & Stroke Association: Stroke, Vol.30, No.4, pp.765-768, 1999.
  16. Radiopaedia (Wiki-based page) [Internet], Http://radiopaedia.org.