스마트미디어저널 (Smart Media Journal)

  • 제5권1호
  • /
  • Pages.49-54
  • /
  • 2016
  • /
  • 2287-1322(pISSN)
  • /
  • 2288-9671(eISSN)
한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
권호 표지

Liver Segmentation and 3D Modeling from Abdominal CT Images

  • Tran, Hong Tai (Dept. of Electronics and Computer Engineering, Chonnam National University) ;
  • Oh, A Ran (Dept. of Electronics and Computer Engineering, Chonnam National University) ;
  • Na, In Seop (Dept. of Electronics and Computer Engineering, Chonnam National University) ;
  • Kim, Soo Hyung (Dept. of Electronics and Computer Engineering, Chonnam National University)
  • 투고 : 2015.11.26
  • 심사 : 2016.03.29
  • 발행 : 2016.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Medical image processing is a compulsory process to diagnose many kinds of disease. Therefore, an automatic algorithm for this task is highly demanded as an important part to construct a computer-aided diagnosis system. In this paper, we introduce an automatic method to segment the liver region from 3D abdominal CT images using Otsu method. First, we choose a 2D slice which has most liver information from the whole 3D image. Secondly, on the chosen slice, we enhanced the image based on its intensity using Otsu method with multiple thresholds and use the threshold to enhance the whole 3D image. Then, we apply a liver mask to mark the candidate liver region. After that, we execute the Otsu method again to segment the liver region from the chosen slice and propagate the result to the whole 3D image. Finally, we apply preprocessing on the frontal side of 3D images to crop only the liver region from the image.

키워드

참고문헌

  1. T. Heimann, et al., Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets, IEEE Transactions on Medical Imaging 28. pp. 1251 - 1265, 2009. https://doi.org/10.1109/TMI.2009.2013851
  2. Anamika Ahirwar, Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI, Information Technology and Computer Science 5. pp. 44 - 53, 2013. https://doi.org/10.5815/ijitcs.2013.05.06
  3. D. Furukawa, A. Shimizu, and H. Kobatake, Automatic liver segmentation based on maximum a posterior probability estimation and level set method, Proc. MICCAI Workshop 3-D Segment at. Clinic: A Grand Challenge. pp. 117 - 124, 2007.
  4. Anter, AM., Azar, AT., Hassanien, AE., El-Bendary, N., ElSoud, M.A, Automatic computer- aided segmentation for liver and hepatic lesions using hybrid segmentations techniques, Computer Science and Information Systems. pp. 193 - 198, 2013.
  5. Nobuyuki Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Sys., Man., Cyber. 9 pp. 62 - 66, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  6. Paoula Campadelli, Elena Kaseraghi, Liver segmentation from computed tomography scans: A survey and a new algorithm, Artificial intelligence in medicine 45. pp. 185 - 196, 2009. https://doi.org/10.1016/j.artmed.2008.07.020
  7. Powers, D.M.W., Evaluation: From precision, recall and F-measure to RoC, informedness, markedness & correlation, Journal of Machine Learning Technologies 2. pp. 37 - 63, 2011.
  8. http://www.sliver07.org