本文選題：脊椎 + 三維模型　； 參考：《光學精密工程》2016年11期

【摘要】：由于脊椎生理結(jié)構(gòu)的精準坐標描述和準確匹配尚未達到醫(yī)學精度的要求,本文對如何精確描述脊柱腰骶段特征點的物理坐標進行研究。介紹了人體脊椎采樣特征點的定義和傳統(tǒng)標記方法。針對手動標注特征點精確度不夠,易產(chǎn)生較大誤差等問題提出了一種基于曲率多特征融合的自適應標注特征點的方法。該方法首先找出某個特征點的高斯曲率和平均曲率流的定義值,得到該特征點的法曲率相對極大值,并計算在指定極小半徑r范圍內(nèi)的所有模型點的法曲率相對極大值。由于極大值曲率越大,三維模型表面在該點處的彎曲程度越大,該點就越能表現(xiàn)三維模型的大致輪廓,故以r范圍內(nèi)極大值最大的點作為特征點的曲率描述來替換手動拾取的點,從而準確反映該點的特征變化情況。最后,對改進結(jié)果進行偏差驗證分析。結(jié)果表明:改進方法的準確度比現(xiàn)有手動標注特征點方法的準確度提高了約37%,驗證了本文方法的有效性。
[Abstract]:Since the precise coordinate description and exact matching of spinal physiological structure have not reached the requirement of medical accuracy, this paper studies how to accurately describe the physical coordinates of the lumbosacral feature points of the spine. This paper introduces the definition and traditional marking method of human spine sampling feature points. In order to solve the problem that the accuracy of manual feature points is not enough and the errors are easy to be generated, a method of adaptive feature point tagging based on multi-feature fusion of curvature is proposed. In this method, the defined values of Gao Si curvature and mean curvature flow of a certain feature point are first found, and the relative maximum of normal curvature of the feature point is obtained, and the relative maximum of normal curvature of all model points in the specified minimum radius r is calculated. Because the greater the maximum curvature, the greater the curvature of the surface of the 3D model at this point, and the more the point can represent the approximate outline of the 3D model. Therefore, the point with the maximum value in r range is used as the curvature description of the feature point to replace the point picked up manually, so as to accurately reflect the characteristic change of the point. Finally, the deviation analysis of the improved results is carried out. The results show that the accuracy of the improved method is about 37% higher than that of the existing manual marking method, and the effectiveness of the proposed method is verified.
【作者單位】： 西北工業(yè)大學計算機學院;西北工業(yè)大學軟件與微電子學院;西安電子科技大學計算機學院;
【基金】：國家自然科學基金資助項目(No.61172147)
【分類號】：TP391.41;R681.5
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本文編號：1944628