•  
  •  
 

Abstract

Semi-Automatic Image Segmentation for Volumetric Visualization of Pelvis CT Scan-Images. The current development of computerized tomography (CT) has enable us to obtain cross sectional image using multi slicing techniques in an order of few seconds. The obtained images represent several tissue structures on cross section slice being imaged. One challenge to help diagnosis using CT images is extracting an anatomic structure of interest using a method of image segmentation and volumetric visualization with the assistance of computers. In case of volumetric visualization of pelvis bones extracted from multi-slice CT images, whole images which are containing part of pelvis bone structures must be segmented. In this research, an image segmentation technique based on active contour is implemented for semi-automatic multi slice image segmentation. Image segmentation steps are initialized with a define model of 2D curve on the first slice image manually. Next, its model curve is deformed to reach the final result of 2D curve that fits to boundary edges of pelvis bone image. The final result of 2D curve on previous slice image was used as an initialization model of 2D curve on the next slice images. This process will continue until the final slice image. This segmentation method was compared with the segmentation method based on threshold from homogenous intensity distribution and manual segmentation method. Quantitative analysis from the results of segmentation on each slice and qualitative analysis on the representation of volumetric visualization are performed in this research.

Bahasa Abstract

Perkembangan terkini dari perangkat pencitraan medik computerized tomography (CT) scan telah memungkinkan dihasilkannya citra dari penampang melintang secara multi irisan dalam orde beberapa detik. Citra medik digital yang dihasilkan merepresentasikan penampang melintang dari berbagai struktur jaringan dari irisan yang dicitrakan. Salah satu tantangan yang dapat membantu dalam proses diagnosis berbasis citra adalah ekstraksi informasi dari struktur anatomi tertentu dengan suatu metode segmentasi citra serta visualisasi volumetrik dengan bantuan komputer. Untuk kasus visualisasi volumetrik tulang pelvis pada citra CT-scan multi irisan, seluruh citra yang mengandung bagian struktur tulang pelvis harus disegmentasi. Pada penelitian ini, satu teknik segmentasi citra berbasis active contour akan diimplementasikan untuk melakukan segmentasi citra multi irisan secara semi otomatis. Proses segmentasi citra diawali dengan menentukan model kurva 2D yang dilakukan secara manual pada citra irisan pertama. Kemudian model kurva tersebut secara iterasi akan berdeformasi sampai dengan bentuk kurva yang berhimpit pada batas tepian citra tulang pelvis. Hari akhir kurva 2D pada irisan pertama akan digunakan sebagai inisialisasi model kurva 2D pada proses segmentasi citra irisan berikutnya. Proses tersebut akan berlanjut sampai dengan citra irisan terakhir. Metode segmentasi citra berbasis active contour akan dibandingkan dengan metode segmentasi secara nilai ambang dari homogenitas distribusi intensitas dan metode segmentasi secara manual. Analisis secara kualitatif terhadap hasil segmentasi tiap irisan dan analisis kualitatif pada representasi visualisasi volumetrik digunakan pada penelitian ini.

References

  1. J.D. Bronzino, The Biomedical Engineering Handbook 2nd Edition, vol. 1, CRC Press, Boca Raton, 2000, p.61.
  2. I. Bankman, Handbook of Medical Imaging: Processing and Analysis, Academic Press, San Diego, USA, 2000, p. 127.
  3. C.F. Westin, L.M. Lorigo, O. Faugeras, W.E.L. Grimson, S. Dawson, A. Norbash, R. Kikinis, In: S.L. Delp, A.M. DiGioia, B. Jaramaz (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2000, Lecture Notes in Computer Science, vol. 1935, Springer Verlag, Berlin, 2000, p. 266.
  4. C. Xu, Department of Electrical and Computer Engineering, Johns Hopkins University, USA, 1999.
  5. F. Derraz, M. Beladgham M. Khelif, Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC ’04), 2 (2004) 675.
  6. M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis, and Machine Vision, International Student Edition, Thomson, Toronto, 2008, p.257.
  7. J. Xie, H. Tsui, W.M.L. Wynnie, Proceedings of the International Conference on Image Processing (ICIP) 4 (2004) 2575.
  8. G. Sundaramoorthi, A. Yezzi, A.C. Mennucci, IEEE Trans. Pattern analysis and Machine Intelligence , 30 (2008) 851.
  9. B. Sumengen, B.S. Manjunath, IEEE Trans. Pattern analysis and Machine Intelligence, 28 (2006) 509.
  10. Y.S. Akgul, C. Kambhamettu, IEEE Trans. Pattern analysis and Machine Intelligence, 25 (2003) 174.
  11. R.V. Cristerna, V.M. Banuelos, O.Y. Suarez, IEEE Trans. On Biomedical Engineering 51 (2004) 459.
  12. Suprijanto, I.M. Farida, Processing the 2th Indonesia Japan Joint Scientific Symposium, Jakarta, Indonesia, 2006.
  13. I.T. Young, J.J. Gerbrands L.J. Van Vliet, Fundamental of Image Processing, The Delft University of Technology, Delft, 2000, p.59.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.