中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (52): 9735-9738.doi: 10.3969/j.issn.1673-8225.2010. 52.012

• 骨与关节图像与影像 bone and joint imaging • 上一篇    下一篇

一种鲁棒的骨龄X射线平片自动轮廓提取方法

冉隆科1，2   

  1. 重庆医科大学，  1基础医学院计算机教研室，2 法医学及生物信息技术研究室，重庆市400016
  • 出版日期:2010-12-24 发布日期:2010-12-24
  • 作者简介:冉隆科★，男，1972年生，土家族，硕士，讲师，主要从事医学数字图像处理、生物医学信息数据挖掘方面的研究。 ranlk@sina.com
  • 基金资助:

    重庆市渝中区科技计划项目资助，题目“骨龄自动评定系统的关键技术研究及应用” 。

An robust automatic contour extraction method for skeletal bone age X-ray images

Ran Long-ke 1,2   

  1. 1 Department of Computer, School of Basic Medicine, 2 Laboratory of Forensics and Biology Information Technology, Chongqing Medical University, Chongqing  400016, China
  • Online:2010-12-24 Published:2010-12-24
  • About author:Ran Long-ke★, Master, Lecturer, Department of Computer, School of Basic Medicine, Laboratory of Forensic and Biology Information Technology, Chongqing Medical University, Chongqing 400016, China ranlk@sina.com
  • Supported by:

     the Science and Technology Development of Yuzhong District, Chongqing

PDF

415

被引次数

7

摘要：

背景：骨龄X射线平片具有不均匀和复杂性，因而在骨龄自动评价的研究中，手掌轮廓提取的结果往往不理想。
目的：采用计算机自动提取手掌轮廓，为骨龄自动评价中图像预处理阶段的研究奠定重要基础。为了解除骨龄评定带来的主观性和不确定性，提出用计算机进行自动评价。
方法：在仔细分析骨龄X射线平片的基础上，提出了一种对图像背景的可行有效子采样点方案，并提出用二元三次线性回归方法来模拟图像背景，通过形态学以及二进制标记等一系列操作，最后成功提取出手掌轮廓。
结果与结论：采用异常点移除和回归方法相结合来提取轮廓，采取固定阀值，不受阀值选取的困扰，并具有鲁棒性。大量的实验结果表明了该方法提取的手掌轮廓成功率在93%以上，能完全应用于骨龄自动识别的后续研究工作。

关键词: 骨龄, 手掌轮廓提取, 二元三次线性回归, 鲁棒, 异常点移除

Abstract:

BACKGROUND: Due to the non-uniform and complexity of bone age X-rays images, the results of contour extraction in automatic skeletal bone age assessment are often unsatisfactory.
OBJECTIVE: To extract palm contour automatically using computer to lay a foundation for image pretreatment of bone age assessment to eliminate subjectivity and uncertainty in bone age assessment.
METHODS: Bone age X-ray film was analyzed, and a feasible and effective program on the background image sub-sampling was proposed. Two-dimensional third order linear regression method was proposed to simulate the background images. Using a series of operations including morphological and binary image labeling, the contour of the hand was successful extracted.
RESULTS AND CONCLUSION: By combining outliers removal with regression, a fixed threshold was used, which avoids selection of appropriate threshold. Moreover, it is robust. Numerous studies have demonstrated that the success rate of hand contour extraction was over 93%, so this method can be fully used in automatic identification of bone age research.

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