本文選題：K-means聚類 + 隨機游走算法�。� 參考：《生物醫(yī)學工程學雜志》2017年06期

【摘要】：光學相干斷層成像技術(OCT)現(xiàn)已發(fā)展成為國內(nèi)外較熱門的冠狀動脈內(nèi)影像技術,其中冠脈OCT圖像的斑塊區(qū)域分割對易損斑塊的識別和研究有著重大意義。本文提出了一種基于K-means聚類與改進隨機游走的新算法,實現(xiàn)了對冠脈鈣化、纖維化斑塊和脂質池的半自動化分割。本文主要創(chuàng)新點為改進了隨機游走算法的權函數(shù),將圖像中像素間的邊與種子點之間的距離加入到了權函數(shù)定義中,增加了弱邊界的權值,防止了過分割現(xiàn)象的發(fā)生。本文基于以上方法對9名冠狀動脈粥樣硬化患者的OCT圖像進行了斑塊區(qū)域分割。通過對比醫(yī)生手動分割結果,證明了本文方法具有良好的精度和魯棒性,以期本文方法可對冠心病的臨床診斷起到一定的輔助作用。
[Abstract]:Optical coherence Tomography (Oct) has developed into a popular intracoronary image technology at home and abroad. The segmentation of plaque region in coronary OCT image is of great significance to the identification and study of vulnerable plaque. In this paper, a new algorithm based on K-means clustering and improved random walk is proposed to realize semi-automatic segmentation of coronary artery calcification, fibrosis plaque and lipid pool. The main innovation of this paper is to improve the weight function of the random walk algorithm. The distance between the edges of pixels and the seed points in the image is added to the definition of the weight function, which increases the weight value of the weak boundary and prevents the phenomenon of over-segmentation. Based on the above methods, OCT images of 9 patients with coronary atherosclerosis were segmented into plaque regions. By comparing the results of manual segmentation by doctors, it is proved that this method has good accuracy and robustness, and it is expected that this method can play an auxiliary role in the clinical diagnosis of coronary heart disease.
【作者單位】： 河北大學電子信息工程學院;中國醫(yī)學科學院北京協(xié)和醫(yī)院心內(nèi)科;
【基金】：國家自然科學基金項目(61473112) 河北省自然科學基金項目(F2015201196) 教育廳科學技術研究計劃(QN2015135);教育廳科學技術研究計劃(QN2014166)
【分類號】：R541.4;TP391.41
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本文編號：1975533