This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionba...This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionbased active contour techniques.In the first method,iterative threshold is performed for manual cropped preprocessed image,and active contour is applied thereafter.To overcome manual cropping in the second method,an automatic seed selection followed by region growing is performed.Given that the result is only a few images owing to over segmentation,the third method uses a fully automatic active contour.Results of the segmentation techniques are compared with the manual markup by experts,specifically by taking the difference in their mean values.Accordingly,the difference in the mean value of the third method is 1.0853,which indicates the closeness of the segmentation.Moreover,the proposed method is compared with the existing fuzzy C means and level set methods.The automatic mass segmentation based on active contour technique results in segmentation with high accuracy.By using adaptive neuro fuzzy inference system,classification is done and results in a sensitivity of 94.73%,accuracy of 93.93%,and Mathew’s correlation coefficient(MCC)of 0.876.展开更多
针对田间玉米冠层叶色变化难以定量描述问题,该文利用田间原位冠层监测系统,在摄像机自动曝光模式下连续采集多个玉米品种的冠层图像,揭示了复杂天气条件对图像和玉米冠层颜色的影响。利用概率密度统计分析方法分别计算玉米6个关键生育...针对田间玉米冠层叶色变化难以定量描述问题,该文利用田间原位冠层监测系统,在摄像机自动曝光模式下连续采集多个玉米品种的冠层图像,揭示了复杂天气条件对图像和玉米冠层颜色的影响。利用概率密度统计分析方法分别计算玉米6个关键生育期的冠层亮度-色度分布,并针对冠层色度具有明确变化趋势且分离度较高的冠层亮度区间,建立了全生育期玉米冠层叶色模型。进而,基于该模型建立了适合不同玉米生育期的冠层图像自动分割方法,将玉米全生育期的冠层图像分割精度提升到82.6%,并揭示了不同品种玉米在叶片发育过程中冠层叶色与叶龄的相关性,利用登海605和农大108的冠层叶色预测出的生育期叶龄均方根误差RMSE(root mean squared error,RMSE)分别为1.14和1.41叶。试验结果表明,该文建立的玉米冠层叶色模型能够较好描述玉米关键生育期的冠层叶色变化规律,对玉米冠层图像分割、生育期估计、玉米品种表型鉴定具有重要意义。展开更多
针对数据场环境下多维数据的低维特征提取问题,本文将数据之间的相互作用纳入其相关性求解中,提出一种基于数据场的典型相关分析(Data field based canonical correlation analysis,DFCCA)方法.DFCCA提取的特征具有良好的分布特性,原空...针对数据场环境下多维数据的低维特征提取问题,本文将数据之间的相互作用纳入其相关性求解中,提出一种基于数据场的典型相关分析(Data field based canonical correlation analysis,DFCCA)方法.DFCCA提取的特征具有良好的分布特性,原空间上相隔较远的数据点对的特征聚集在一个较小区域内,而相邻数据点对的特征却有规律地分布在其他点所聚集区域的周围.此特性使得DFCCA具有较好的边界辨识能力,将其应用于图像分割的实验结果表明,DFCCA提取的复杂图像边界具有较好的保真度.展开更多
文摘This research implements a novel segmentation of mammographic mass.Three methods are proposed,namely,segmentation of mass based on iterative active contour,automatic region growing,and fully automatic mask selectionbased active contour techniques.In the first method,iterative threshold is performed for manual cropped preprocessed image,and active contour is applied thereafter.To overcome manual cropping in the second method,an automatic seed selection followed by region growing is performed.Given that the result is only a few images owing to over segmentation,the third method uses a fully automatic active contour.Results of the segmentation techniques are compared with the manual markup by experts,specifically by taking the difference in their mean values.Accordingly,the difference in the mean value of the third method is 1.0853,which indicates the closeness of the segmentation.Moreover,the proposed method is compared with the existing fuzzy C means and level set methods.The automatic mass segmentation based on active contour technique results in segmentation with high accuracy.By using adaptive neuro fuzzy inference system,classification is done and results in a sensitivity of 94.73%,accuracy of 93.93%,and Mathew’s correlation coefficient(MCC)of 0.876.
文摘针对田间玉米冠层叶色变化难以定量描述问题,该文利用田间原位冠层监测系统,在摄像机自动曝光模式下连续采集多个玉米品种的冠层图像,揭示了复杂天气条件对图像和玉米冠层颜色的影响。利用概率密度统计分析方法分别计算玉米6个关键生育期的冠层亮度-色度分布,并针对冠层色度具有明确变化趋势且分离度较高的冠层亮度区间,建立了全生育期玉米冠层叶色模型。进而,基于该模型建立了适合不同玉米生育期的冠层图像自动分割方法,将玉米全生育期的冠层图像分割精度提升到82.6%,并揭示了不同品种玉米在叶片发育过程中冠层叶色与叶龄的相关性,利用登海605和农大108的冠层叶色预测出的生育期叶龄均方根误差RMSE(root mean squared error,RMSE)分别为1.14和1.41叶。试验结果表明,该文建立的玉米冠层叶色模型能够较好描述玉米关键生育期的冠层叶色变化规律,对玉米冠层图像分割、生育期估计、玉米品种表型鉴定具有重要意义。
文摘针对数据场环境下多维数据的低维特征提取问题,本文将数据之间的相互作用纳入其相关性求解中,提出一种基于数据场的典型相关分析(Data field based canonical correlation analysis,DFCCA)方法.DFCCA提取的特征具有良好的分布特性,原空间上相隔较远的数据点对的特征聚集在一个较小区域内,而相邻数据点对的特征却有规律地分布在其他点所聚集区域的周围.此特性使得DFCCA具有较好的边界辨识能力,将其应用于图像分割的实验结果表明,DFCCA提取的复杂图像边界具有较好的保真度.
