基于圖割和邊緣行進(jìn)的肝臟CT序列圖像分割

廖苗; 趙于前; 曾業(yè)戰(zhàn); 黃忠朝; 鄒北驥

doi:10.11999/JEIT151005

基于圖割和邊緣行進(jìn)的肝臟CT序列圖像分割

doi: 10.11999/JEIT151005

1.
(中南大學(xué)地球科學(xué)與信息物理學(xué)院長(zhǎng)沙 410083) ②(中南大學(xué)信息科學(xué)與工程學(xué)院長(zhǎng)沙 410083)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61172184, 61379107, 61402539, 61174210)，新世紀(jì)優(yōu)秀人才支持計(jì)劃(NCET-13-0603)，高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金(20130162110016)，湖南省科技基本建設(shè)項(xiàng)目(20131199)，湖南省科技計(jì)劃項(xiàng)目(2015RS4008)，中南大學(xué)中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(2014ZZTS053)，湖南省研究生科研創(chuàng)新項(xiàng)目(CX2014B052)

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出版歷程
- 收稿日期: 2015-09-08
- 修回日期: 2016-01-22
- 刊出日期: 2016-06-19

Liver Segmentation from Abdominal CT Volumes Based on Graph Cuts and Border Marching

1.
(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

Funds:

The National Natural Science Foundation of China (61172184, 61379107, 61402539, 61174210), Program for New Century Excellent Talents in University of Ministry of Education in China (NCET-13-0603), Specialized Research Fund for the Doctoral Program of Higher Education in China (20130162110016), Program for Hunan Province Science and Technology Basic Construction (Grant 20131199), Hunan Provincial Science and Technology Project of China (2015RS4008), Fundamental Research Funds for the Central Universities of Central South University (2014ZZTS053), Hunan Provincial Innovation Foundation for Postgraduate (CX2014B052)

摘要

摘要: 提出一種新的基于圖割和邊緣行進(jìn)的腹部CT序列圖像肝臟分割方法。首先，針對(duì)輸入序列的數(shù)據(jù)特征，建立肝臟亮度和外觀模型，突出肝臟區(qū)域抑制非肝臟區(qū)域；然后，將肝臟亮度、外觀模型以及相鄰切片之間的位置信息有效融入圖割能量函數(shù)，實(shí)現(xiàn)CT序列肝臟的自動(dòng)初步分割；最后，針對(duì)血管欠分割問題，提出了一種基于邊緣行進(jìn)的結(jié)果優(yōu)化方法。通過對(duì)XHCSU14和SLIVER07數(shù)據(jù)庫(kù)提供的30個(gè)病人肝臟序列的分割實(shí)驗(yàn)，以及與其他多種肝臟分割方法的比較，表明該方法能完整有效地分割肝臟，準(zhǔn)確性高，魯棒性強(qiáng)。
- 醫(yī)學(xué)圖像分割 /
- 圖割 /
- 邊緣行進(jìn) /
- 高斯擬合 /
- 主成分分析
Abstract: A novel method for liver segmentation from abdominal CT volumes based on graph cuts and border marching is proposed. First, to exclude complex background and highlight liver region, liver intensity and appearance models are built according to the characteristics of a given CT volume. Then, the intensity and appearance models together with location information from neighbor segmented slice are effectively integrated into graph cuts cost computation to segment the CT volume initially and automatically. Finally, to solve the under-segmentation issue of liver vessel, a boundary compensation method based on border marching is proposed. The proposed method is tested and compared with some other methods on 30 CT volumes from XHCSU14 and SLIVER07 databases. The experimental results show that the proposed method can segment livers integrally and effectively from abdominal CT volumes, with higher accuracy and robustness.
- Medical image segmentation /
- Graph cuts /
- Border marching /
- Gaussian fitting /
- Principal Components Analysis (PCA)

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參考文獻(xiàn)(13)

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