Liver segmentation and vessel extraction from 3D CT images

在三維電腦斷層影像上進行肝臟切割及血管抽取

鄭雅勻(Ya-yun Cheng)

指導教授 ： 賴尚宏

國立清華大學/電機資訊學院/資訊系統與應用研究所/碩士(2012年)

摘要

在這篇論文中，我們提出一個用在電腦斷層的腹部影像上切割出肝臟的位置，以及擷取出肝臟內部的血管並建立相對應的3D立體模型以供醫生方便了解病人的器官位置，降低手術時間。我們的系統包含兩個部分，第一部分是一個利用病人特徵資訊結合區域增長的演算法來切出肝臟的位置，之後再利用3D空間中的隨機漫步法以及專家所給予的少許適當的標記點來優化切割出位置的可信度並增加對於不正常肝臟影像的使用性。在第二部分則包含，我們修改一個基於幾何空間特性的管狀濾波器，使其相比於原始的管狀濾波器，對於有病變組織存在的肝臟能有更佳的容忍度。我們藉由人體血管結構的一些自然特性來決定濾波器反應的可信度，進而擷取出血管的位置。我們所提出的系統應用在臨床影像上可得到相當的準確度。

關鍵字

肝臟切割 ； 電腦斷層 ； 血管擷取 ； 3D模型建立

並列摘要

In this thesis, we present a system for liver segmentation and vessel structure extraction from 3D Computer Tomography (CT) images with the corresponding 3D model reconstruction. For clinical application, we want the system can be controlled or modified by the radiologists or doctors, so we purpose a semi-automatic method to segment the liver area which is based on a region growing technique combined with 3D random walker algorithm for interactive segmentation refinement. The experimental results show higher accuracy in the segmentation results by using the proposed algorithm. After obtaining the liver segmentation result, we use a modified tubular filter to extract the vessel structure which is more robust than the original one. Using some special characteristics of the human vessel structure, we achieve more accurate vessel extraction for 3D model reconstruction.

並列關鍵字

liver segmentation ； Computer tomography ； Vessel extraction ； 3D model reconstruction

參考文獻

[2] A. Fruzan, R. Aghaeizadeh Zoroofi, M. Hori, and Y. Sato, “Liver segmentation by intensity analysis and anatomical information in multi-slice CT images.,” International Journal of Computer Assisted Radiology and Surgery, vol. 4, no. 3, pp. 287-297, 2009.

[4] S.J Lim, Y.Y. Jeong, C.W. Lee, Y.S. Ho “Automatic segmentation of the liver in CT images using the watershed algorithm based on morphological filtering.” SPIE Proc., vol.5370, pp. 1658–1666, 2004.

[5] S.J. Lim, Y.Y. Jeong, Y.S. Ho,” Automatic liver segmentation for volume measurement in CT images”, J. Vis. Commun. Image R., 17, pp. 860–875, 2006.

[6] T. Heimann, H. Meinzer “Statistical shape models for 3D medical image segmentation: A review.”, Medical Image Analysis, vol.13, np.4, pp. 543-563, 2009.

[9] X. Zhang, J. Tian, K. Deng, W. Wu and X. Li, ”Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection.”, IEEE Trans. Biomed. Eng., vol. 57, no. 10, pp. 2622-2626, 2010.