本文選題：氣管樹分割　切入點(diǎn)：區(qū)域生長　出處：《東北大學(xué)》2013年碩士論文

【摘要】：慢性阻塞性肺病(慢阻肺)是發(fā)病數(shù)量最多的呼吸系統(tǒng)疾病之一,不僅嚴(yán)重影響患者身體健康和生活質(zhì)量,而且給患者家庭和社會(huì)造成很大經(jīng)濟(jì)負(fù)擔(dān)。隨著多層螺旋CT (MSCT)技術(shù)的出現(xiàn)和發(fā)展,基于MSCT影像的定量評(píng)估可檢測肺部組織病變程度,對(duì)發(fā)病機(jī)理進(jìn)行長期研究,跟蹤評(píng)估治療效果,成為慢阻肺診斷和治療的有力工具。氣管樹分割是基于MSCT影像的慢阻肺定量評(píng)估系統(tǒng)中的關(guān)鍵技術(shù),從大量MSCT影像中實(shí)時(shí)準(zhǔn)確地獲取氣管樹數(shù)據(jù)已成為當(dāng)前迫切需要解決的難題。在上述背景下,本文以MSCT圖像為數(shù)據(jù)源,肺部氣管樹為主要研究對(duì)象,對(duì)肺功能分析輔助診斷中涉及到的氣管樹分割與中心線提取算法進(jìn)行了設(shè)計(jì)與實(shí)現(xiàn)。首先,本文對(duì)課題所涉及的氣管樹解剖學(xué)與圖像學(xué)先驗(yàn)知識(shí)、醫(yī)學(xué)圖像分割、中心線提取、運(yùn)動(dòng)目標(biāo)跟蹤等背景知識(shí)進(jìn)行介紹；其次,設(shè)計(jì)并實(shí)現(xiàn)了基于最大類間方差和多尺度三維區(qū)域生長的氣管樹自動(dòng)分割算法,提取氣管樹主干數(shù)據(jù)；再次,根據(jù)數(shù)字拓?fù)鋵W(xué)原理設(shè)計(jì)并實(shí)現(xiàn)了基于拓?fù)浼?xì)化的氣管樹中心線提取算法,引用SWC格式標(biāo)準(zhǔn)獲得中心線拓?fù)浣Y(jié)構(gòu),利用最小二乘法擬合中心線分支方向；最后,將運(yùn)動(dòng)目標(biāo)跟蹤技術(shù)應(yīng)用于氣管樹分割領(lǐng)域,在獲得數(shù)據(jù)基礎(chǔ)上設(shè)計(jì)并實(shí)現(xiàn)了基于Kalman濾波的氣管樹末梢分割算法。實(shí)驗(yàn)表明,本文提出的氣管樹分割算法具有良好精度與速度,滿足慢阻肺臨床輔助診斷需求。
[Abstract]:Chronic obstructive pulmonary disease (COPD) is one of the most common respiratory diseases, which not only seriously affects the health and quality of life of patients. With the emergence and development of multi-slice spiral CT (MSCT) technology, quantitative assessment based on MSCT images can detect the degree of lung tissue lesions and study the pathogenesis of the disease. Tracheal tree segmentation is a key technique in quantitative assessment system of chronic obstructive pulmonary disease (COPD) based on MSCT image. Obtaining trachea tree data in real time and accurately from a large number of MSCT images has become an urgent problem. Under the above background, this paper takes MSCT image as data source and lung trachea tree as main research object. The trachea tree segmentation and centerline extraction algorithms are designed and implemented in lung function analysis assisted diagnosis. Firstly, the prior knowledge of trachea tree anatomy and imageology, medical image segmentation and centerline extraction are discussed in this paper. Background knowledge such as moving target tracking is introduced. Secondly, an automatic trachea tree segmentation algorithm based on maximum inter-class variance and multi-scale three-dimensional region growth is designed and implemented to extract trachea tree trunk data. According to the principle of digital topology, a trachea tree centerline extraction algorithm based on topology thinning is designed and implemented. The center line topology structure is obtained by using SWC format standard, and the center line branch direction is fitted by the least square method. The trachea tree segmentation algorithm based on Kalman filter is designed and implemented by applying the moving target tracking technology to trachea tree segmentation. Experiments show that the proposed trachea tree segmentation algorithm has good accuracy and speed. To meet the needs of clinical diagnosis of COPD.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：R563.9;TP391.41

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 王昌;高精度肺部氣道樹的分割及骨架中心線的提取[D];中國科學(xué)技術(shù)大學(xué);2010年

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本文編號(hào)：1657427