Abdomen CT Image Segmentation Based on MRF and Ribs Fitting Approach
Abdomen CT Image Segmentation Based on MRF and Ribs Fitting Approach
复制标题
基于MRF和肋骨拟合方法的腹部CT图像分割
DOI：
10.1007/978-1-4471-4790-9_10
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Xiangrong Zhou
中科院分区：
文献类型：
--
作者：
Huiyan Jiang;Zhiyuan Ma;Mao Zong;H. Fujita;Xiangrong Zhou
针对边缘模糊的肝脏图像分割问题，提出了一种基于马尔可夫随机场和肋骨拟合的分割算法。新算法包括三个主要步骤。首先，对腹部图像进行预处理，以拟合肋骨并去除阻塞区域。然后采用提升小波变换对图像进行不同分辨率的分解，对低频子图像采用基于马尔可夫随机场的图像分割算法，最后采用形态学运算得到肝脏区域。小波域的初始分割和多层分割算法有K-means和MAP/ICM。实验结果表明，该算法具有较好的鲁棒性，分割精度高于传统的MRF方法。
Aiming at the segmentation of liver image with fuzzy edge, a new algorithm based on Markov Random Field and ribs fitting approach is proposed. The new algorithm consists of three main steps. Firstly, an abdominal image is pre-processed to fit ribs and remove the obstructive region. Then, lifting wavelet transform is adopted to decompose an image in different resolutions, and an image segmentation algorithm based on MRF is manipulated to the low frequency sub-images; lastly, morphology operation is adopted to obtain the liver region. The algorithms of the initial and multi-level segmentation in wavelet domain are K-means and MAP/ICM. Several experiments have been carried out and the experimental results show that the proposed algorithm has a good robustness and higher segmentation accuracy than the traditional MRF approach.