采用最小误差阈值分割算法的基因芯片图像分析

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.

Image analysis of gene chip using minimum error threshold segmentation algorithm

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.