[1]尹宁,刘富,张玉.采用最小误差阈值分割算法的基因芯片图像分析[J].智能系统学报,2013,8(1):28-32.[doi:10.3969/j.issn.1673-4785.201207015]
YIN Ning,LIU Fu,ZHANG Yu.Image analysis of gene chip using minimum error threshold segmentation algorithm[J].CAAI Transactions on Intelligent Systems,2013,8(1):28-32.[doi:10.3969/j.issn.1673-4785.201207015]
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YIN Ning,LIU Fu,ZHANG Yu.Image analysis of gene chip using minimum error threshold segmentation algorithm[J].CAAI Transactions on Intelligent Systems,2013,8(1):28-32.[doi:10.3969/j.issn.1673-4785.201207015]
采用最小误差阈值分割算法的基因芯片图像分析
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
8
期数:
2013年第1期
页码:
28-32
栏目:
学术论文—机器学习
出版日期:
2013-03-25
- Title:
- Image analysis of gene chip using minimum error threshold segmentation algorithm
- 文章编号:
- 1673-4785(2013)01-0028-05
- Keywords:
- gene chip image; image analysis; minimum error threshold segmentation method; cluster analysis
- 分类号:
- TP391.41
- 文献标志码:
- A
- 摘要:
- 为了能够较好地处理芯片图像，尽可能准确地提取出描述基因样点的数据信息，采用了最小误差阈值的分割算法.该方法在假设目标和背景的分布服从混合正态分布的前提下，设定了最小误差分类目标函数，通过求得使目标函数值最小的最佳分割阈值，实现基因样点和背景图像的分割.针对分割出来的基因样点图像提取特征数据，最后对这些数据进行聚类分析，进而对实验样点进行分类.在实验中应用该方法分析了2组基因芯片图像，基因样点的分类效果较好，验证了该基因芯片分析方法的可行性.
- Abstract:
- In order to analyze gene chip image better, along with extract the data information as accurately as possible, to describe the gene sample, this research paper proposes to implement a minimum error threshold segmentation method. Based on the assumption that the distributions of object and background are governed by a mixture normal distribution, this method sets an objective function of minimum error classification. This method also allows for the implementation of the segmentation between gene sample and background image through calculating the optimal segmentation threshold by minimizing the objective function. Next, the feature data from the segment of gene sample image was extracted and a clustering analysis with the data was done to realize the successful classification of the experimental samples. The study examined two groups of gene chip images and analyzed them by using this method in the experiment. The results show that the classification result was better and the feasibility of the analysis method was verified.
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备注/Memo
收稿日期： 2012-07-03.
网络出版日期：2013-01-25.
基金项目：吉林省科技发展计划资助项目（10100505）.
通信作者：刘富. E-mail: liufu@jlu.edu.cn.
作者简介：
尹宁，女，1985年生，硕士研究生，主要研究方向为图像处理与模式识别.
刘富，男，1968年生，教授，博士生导师，吉林省自动化学会秘书长.主要研究方向为计算机视觉与模式识别、生物信息识别技术等.承担国家重大科技成果转化等项目30余项，获得国家发明专利2项，发表学术论文40余篇.
张玉，女，1989年生，硕士研究生，主要研究方向为图像处理与模式识别.
更新日期/Last Update:
2013-04-12