基于序列間先驗(yàn)約束和多視角信息融合的肝臟CT圖像分割

彭佳林; 揭萍

doi:10.11999/JEIT170933

基于序列間先驗(yàn)約束和多視角信息融合的肝臟CT圖像分割

doi: 10.11999/JEIT170933 cstr: 32379.14.JEIT170933

彭佳林¹,
揭萍¹

基金項(xiàng)目:

國家自然科學(xué)基金(11771160, 11401231)，福建省自然科學(xué)基金面上項(xiàng)目(2015J01254)，華僑大學(xué)中青年教師科技創(chuàng)新資助計(jì)劃項(xiàng)目(ZQN-PY411)

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- 文章訪問數(shù): 1283
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-10-09
- 修回日期: 2018-02-06
- 刊出日期: 2018-04-19

Liver Segmentation from CT Image Based on Sequential Constraint and Multi-view Information Fusion

PENG Jialin¹,
JIE Ping ¹

Funds:

The National Natural Science Foundation of China (11771160, 11401231), The Natural Science Foundation of Fujian Province (2015J01254), The Research Promotion Program of Huaqiao University (ZQN-PY411)

摘要

摘要: 醫(yī)學(xué)電子計(jì)算機(jī)斷層掃描(CT)序列圖像中肝臟的準(zhǔn)確分割是實(shí)現(xiàn)計(jì)算機(jī)輔助肝手術(shù)的重要前提，然而圖像中存在的組織病變、邊界模糊或缺失、不同組織間的粘連給肝臟分割帶來極大挑戰(zhàn)。針對這些問題，該文提出一種基于圖像序列間先驗(yàn)約束的半自動分割方法，并進(jìn)一步采取了多視角信息融合的方式實(shí)現(xiàn)肝臟的準(zhǔn)確分割。該方法的優(yōu)勢在于無需大量數(shù)據(jù)的收集和復(fù)雜的先驗(yàn)訓(xùn)練。在Sliver07公開數(shù)據(jù)集合的驗(yàn)證結(jié)果顯示，和領(lǐng)域內(nèi)主要方法相比，該方法具有較高的分割準(zhǔn)確度，特別是當(dāng)肝臟區(qū)域存在病灶、邊界模糊或缺失的情況下具有明顯提升。
- CT序列圖像 /
- 肝臟分割 /
- 先驗(yàn)約束 /
- 多視角信息融合
Abstract: The accurate segmentation of liver in medical Computed Tomography (CT) sequence images is important prerequisite for computer-assisted liver surgery. However, the presence of tissue lesions, the blurred or missing boundary and the adhesion between different organs/tissues poses great challenges to liver segmentation. To address these problems, this paper presents a semi-automatic segmentation method based on the sequential constraints of image sequences, and introduces further a multi-view information fusion method to achieve the accurate segmentation of the liver. One advantage of this approach is that it does not need extensive data collection and complicated prior training. The validation and comparison results on the Sliver07 public data show that the proposed method shows competitive performance, especially when there is liver tumor, blurred or missing liver boundary.
- CT sequence image /
- Liver segmentation /
- Prior constraint /
- Multi-view information fusion

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參考文獻(xiàn)(25)

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