發(fā)布時間：2018-06-04 22:49

  本文選題：小動物影像分析 + 骨關節(jié)點定位�。� 參考：《中國生物醫(yī)學工程學報》2017年03期

【摘要】：隨著小動物成像技術的發(fā)展,技術人員每天需要處理的小動物影像數(shù)量急劇增長,這使得自動化的小動物圖像分析方法成為迫切的需求。在小鼠圖像分析方面,小鼠靈活多變的身體姿態(tài)給自動化的圖像分析帶來困難�；陔S機森林算法實現(xiàn)小鼠micro-CT圖像中骨骼關節(jié)點的自動定位,為解決小鼠影像中身體姿態(tài)的自動識別打下基礎。該算法主要分3步:先通過分類隨機森林算法得到小鼠骨骼關節(jié)點的粗定位,再通過回歸隨機森林算法進一步減小定位誤差,最后通過圖匹配的方法在備選點中挑選正確位置上的關節(jié)點。對49例不同身體姿態(tài)的小鼠全身三維micro-CT圖像進行測試,全身關節(jié)點定位的成功率為98.27%,定位誤差的中值為0.68 mm。同時驗證聯(lián)合使用分類與回歸隨機森林的必要性,并探究訓練數(shù)據(jù)的數(shù)量對不同骨關節(jié)的識別效果的影響。研究為小鼠micro-CT影像中身體姿態(tài)的識別提供一種新方法,為后續(xù)的自動化圖像配準、圖像分割以及自動化圖像測量提供重要的定位信息。
[Abstract]:With the development of small animal imaging technology, the number of small animal images that technicians need to deal with every day increases rapidly, which makes the automatic small animal image analysis method become an urgent need. In the aspect of image analysis of mice, the flexible and changeable body posture of mice makes it difficult to automate image analysis. Based on the stochastic forest algorithm, the automatic location of the skeletal node in mouse micro-CT image is realized, which lays the foundation for automatic recognition of body posture in mouse image. The algorithm is mainly divided into three steps: firstly, the rough location of mouse skeletal node is obtained by classifying stochastic forest algorithm, and then the localization error is further reduced by regression stochastic forest algorithm. Finally, the correct position of the node is selected in the alternative point by the method of graph matching. Three-dimensional micro-CT images of 49 mice with different body posture were tested. The success rate of locating the whole body knots was 98.27 mm, and the median of positioning error was 0.68 mm. At the same time, the necessity of combined use of classification and regression random forest was verified, and the effect of the amount of training data on the recognition effect of different bone joints was explored. This study provides a new method for the recognition of body posture in mouse micro-CT images, and provides important location information for subsequent automated image registration, image segmentation and automatic image measurement.
【作者單位】： 大連理工大學生物醫(yī)學工程系;
【基金】：國家自然科學基金(61571076);國家自然科學基金青年基金(81401475) 遼寧省自然科學基金(2015020040)
【分類號】：R814;TP391.41

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2 張紅巖;隨機森林在醫(yī)學影像數(shù)據(jù)分析中的應用[D];湖南師范大學;2013年

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本文編號：1979169