發(fā)布時(shí)間：2018-06-04 22:49

  本文選題：小動(dòng)物影像分析 + 骨關(guān)節(jié)點(diǎn)定位�。� 參考：《中國生物醫(yī)學(xué)工程學(xué)報(bào)》2017年03期

【摘要】：隨著小動(dòng)物成像技術(shù)的發(fā)展,技術(shù)人員每天需要處理的小動(dòng)物影像數(shù)量急劇增長,這使得自動(dòng)化的小動(dòng)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(dòng)化的圖像分析帶來困難�；陔S機(jī)森林算法實(shí)現(xiàn)小鼠micro-CT圖像中骨骼關(guān)節(jié)點(diǎn)的自動(dòng)定位,為解決小鼠影像中身體姿態(tài)的自動(dòng)識(shí)別打下基礎(chǔ)。該算法主要分3步:先通過分類隨機(jī)森林算法得到小鼠骨骼關(guān)節(jié)點(diǎn)的粗定位,再通過回歸隨機(jī)森林算法進(jìn)一步減小定位誤差,最后通過圖匹配的方法在備選點(diǎn)中挑選正確位置上的關(guān)節(jié)點(diǎn)。對(duì)49例不同身體姿態(tài)的小鼠全身三維micro-CT圖像進(jìn)行測(cè)試,全身關(guān)節(jié)點(diǎn)定位的成功率為98.27%,定位誤差的中值為0.68 mm。同時(shí)驗(yàn)證聯(lián)合使用分類與回歸隨機(jī)森林的必要性,并探究訓(xùn)練數(shù)據(jù)的數(shù)量對(duì)不同骨關(guān)節(jié)的識(shí)別效果的影響。研究為小鼠micro-CT影像中身體姿態(tài)的識(shí)別提供一種新方法,為后續(xù)的自動(dòng)化圖像配準(zhǔn)、圖像分割以及自動(dòng)化圖像測(cè)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(xué)生物醫(yī)學(xué)工程系;
【基金】：國家自然科學(xué)基金(61571076);國家自然科學(xué)基金青年基金(81401475) 遼寧省自然科學(xué)基金(2015020040)
【分類號(hào)】：R814;TP391.41

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本文編號(hào)：1979169