图像识别技术在猪蓝耳病诊断中的应用研究
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摘要
从PRRS诊断出发,通过图像识别技术对淋巴结细胞含量水平进行分析,从而辅助PRRS的诊断。主要研究内容如下：
(1)研究了目前PRRS的国内外研究现状以及图像识别技术在医疗诊断领域的研究现状。
(2)图像预处理研究。由于医学图像的复杂性和实验环境的影响,不能将原始医学图像用于图像分割。针对不均匀光照的问题,引入了HSI颜色空间模型,解除了彩色信息和光强度的关系。对常用的滤波算法和边缘检测方法进行系统的对比研究,对数学形态学进行了简要介绍。
(3)基于分水岭的图像分割改进算法研究。在平滑滤波方面,通过实验选取双边滤波作为分水岭算法的平滑算法,减少了过分割区域。在标记提取方面,引入人工启发信息,基于HSI彩色图像分割并结合形态学操作设计了标记提取方法,得到了有意义的细胞标记。在区域合并方面,针对传统区域合并的高时间消耗问题,引入了标记信息,设计了基于区域生长的合并方法。在设计区域合并的距离度量时,充分利用了彩色图像的丰富信息,从颜色、纹理、公共边缘三方面设计了区域合并的准则,取得了较好的效果。
(4)特征系统提取方法及特征规约研究。从形状、纹理、颜色三方面对细胞进行了系统的特征提取。结合PCA特征变换和ReliefF特征选择各自的优点,提出了一种基于PCA和ReliefF的特征规约方法,该方法有效地降低了特征维数,消除了特征间的不相关性,并剔除了对分类预测贡献较小的特征。
(5)基于分水岭的图像分割改进算法研究。对于淋巴结这样的复杂医学图像,单个分类器识别效率并不理想。而集成学习能够显著地提升分类器的性能,故对其进行了深入研究。针对AdaBoost对噪声敏感的特性,提出了基于聚类的样本去噪方法,提升了Adaboost算法的稳定性及识别准确率。并进一步提出了基于聚类的大规模样本集简样方法。还从提高基分类器差异度角度入手,在AdaBoost和Bagging算法中引入随机扰动,进一步提升了学习器的泛化能力。综合上述研究成果,利用加权投票设计了二级分类器集成方法,并基于模块化设计和管道思想提出了一套完整的模式识别算法框架。
(6)图像识别技术在医疗诊断中的应用研究。对实验结果得到的细胞含量水平进行了分析,指出其进行疾病诊断的指标,为PRRS诊断提供了依据。
Aimed at diagnosis of PRRS,we use the image recognition technology to analyze the content level of lymph node cells,which can assit the the diagnosis of PRRS. The main contents are as follows:
(1)In this paper,we have survey the domestic and international research status of PRRS,as well as the image recognition technology in the field of medical diagnostics.
(2) Image pre-processing research. Due to the complexity of medical images and experimental environment,we can not use the original medical images for image segmentation directly. In this paper, in view of problems such as uneven illumination,we have introduced the HSI color space model,which lift the relationship between color information and light intensity. We have also investigated commonly used smoothing filter methods、edge detection methods and the binary morphology.
(3) Researchof image segmentation based on watershed algorithm. a) Smoothing filter:we select bilateral filter as the smoothing algorithm before the watershed algorithm by experiment.b) marker extraction:we introduced the artificial heuristic informationto marker extraction,and designed the extraction method of the mark based on the HSI color image segmentation and morphological operations, thus obtained meaningful cell markers.c) Region merging:aiming at the high time consumption problem for the traditional region merging method, we introduced the marker information,and designed a new region merging method based on region growing. When considering the design of the distance between regions,we take full advantage of the abundant information of the color image, put a region merging criteria from three aspects of color, texture, and public edge, and achieved good results. For the adhesive cells in the image, we also adopt the adhesive cell separation method based on searching the concave point.
(4) Research of systemicly feature extraction and reduction. We carried the feature extraction systemicly from three aspects of shape, texture and color. In this paper, We combined the advantages of PCA feature transform and ReliefF feature selection, and proposed a the feature reduction method based on PCA and ReliefF, the method reduced the feature dimension effectively, eliminating the correlation between the features, and removing features which contributes little to the classification.
(5) Research of classification based on integrated learning as well as sample denoising and reduction method. The complexity of medical images such as the lymph nodes,the recognition result by single classifier is not satisfactory. Integrated learning can significantly improve the performance of the classifiers, so we carried out in-depth study on integrated learning, described the classic Boosting and Bagging algorithm. For noise-sensitive characteristics of Adaboost, we proposed denoising method based on clustering of samples, and further research on large-scale sample set reduction methods based on clustering.Starting with improving the difference of the base classifiers, we introduce random perturbations to further enhance the learning generalization ability in Adaboost and Bagging method. At last, we use weighted voting to design the integrated approach of the two level classifiers, and proposed a complete set of pattern recognition algorithm framework based on modular design and pipe ideology.
(6)Research of application of image recognition technology in medical diagnostics. We analyze the cell concentration levels obtained by experiments,and point out the diagnostic indicators of disease, provides a basis for PRRS diagnosis.
(1)研究了目前PRRS的国内外研究现状以及图像识别技术在医疗诊断领域的研究现状。
(2)图像预处理研究。由于医学图像的复杂性和实验环境的影响,不能将原始医学图像用于图像分割。针对不均匀光照的问题,引入了HSI颜色空间模型,解除了彩色信息和光强度的关系。对常用的滤波算法和边缘检测方法进行系统的对比研究,对数学形态学进行了简要介绍。
(3)基于分水岭的图像分割改进算法研究。在平滑滤波方面,通过实验选取双边滤波作为分水岭算法的平滑算法,减少了过分割区域。在标记提取方面,引入人工启发信息,基于HSI彩色图像分割并结合形态学操作设计了标记提取方法,得到了有意义的细胞标记。在区域合并方面,针对传统区域合并的高时间消耗问题,引入了标记信息,设计了基于区域生长的合并方法。在设计区域合并的距离度量时,充分利用了彩色图像的丰富信息,从颜色、纹理、公共边缘三方面设计了区域合并的准则,取得了较好的效果。
(4)特征系统提取方法及特征规约研究。从形状、纹理、颜色三方面对细胞进行了系统的特征提取。结合PCA特征变换和ReliefF特征选择各自的优点,提出了一种基于PCA和ReliefF的特征规约方法,该方法有效地降低了特征维数,消除了特征间的不相关性,并剔除了对分类预测贡献较小的特征。
(5)基于分水岭的图像分割改进算法研究。对于淋巴结这样的复杂医学图像,单个分类器识别效率并不理想。而集成学习能够显著地提升分类器的性能,故对其进行了深入研究。针对AdaBoost对噪声敏感的特性,提出了基于聚类的样本去噪方法,提升了Adaboost算法的稳定性及识别准确率。并进一步提出了基于聚类的大规模样本集简样方法。还从提高基分类器差异度角度入手,在AdaBoost和Bagging算法中引入随机扰动,进一步提升了学习器的泛化能力。综合上述研究成果,利用加权投票设计了二级分类器集成方法,并基于模块化设计和管道思想提出了一套完整的模式识别算法框架。
(6)图像识别技术在医疗诊断中的应用研究。对实验结果得到的细胞含量水平进行了分析,指出其进行疾病诊断的指标,为PRRS诊断提供了依据。
Aimed at diagnosis of PRRS,we use the image recognition technology to analyze the content level of lymph node cells,which can assit the the diagnosis of PRRS. The main contents are as follows:
(1)In this paper,we have survey the domestic and international research status of PRRS,as well as the image recognition technology in the field of medical diagnostics.
(2) Image pre-processing research. Due to the complexity of medical images and experimental environment,we can not use the original medical images for image segmentation directly. In this paper, in view of problems such as uneven illumination,we have introduced the HSI color space model,which lift the relationship between color information and light intensity. We have also investigated commonly used smoothing filter methods、edge detection methods and the binary morphology.
(3) Researchof image segmentation based on watershed algorithm. a) Smoothing filter:we select bilateral filter as the smoothing algorithm before the watershed algorithm by experiment.b) marker extraction:we introduced the artificial heuristic informationto marker extraction,and designed the extraction method of the mark based on the HSI color image segmentation and morphological operations, thus obtained meaningful cell markers.c) Region merging:aiming at the high time consumption problem for the traditional region merging method, we introduced the marker information,and designed a new region merging method based on region growing. When considering the design of the distance between regions,we take full advantage of the abundant information of the color image, put a region merging criteria from three aspects of color, texture, and public edge, and achieved good results. For the adhesive cells in the image, we also adopt the adhesive cell separation method based on searching the concave point.
(4) Research of systemicly feature extraction and reduction. We carried the feature extraction systemicly from three aspects of shape, texture and color. In this paper, We combined the advantages of PCA feature transform and ReliefF feature selection, and proposed a the feature reduction method based on PCA and ReliefF, the method reduced the feature dimension effectively, eliminating the correlation between the features, and removing features which contributes little to the classification.
(5) Research of classification based on integrated learning as well as sample denoising and reduction method. The complexity of medical images such as the lymph nodes,the recognition result by single classifier is not satisfactory. Integrated learning can significantly improve the performance of the classifiers, so we carried out in-depth study on integrated learning, described the classic Boosting and Bagging algorithm. For noise-sensitive characteristics of Adaboost, we proposed denoising method based on clustering of samples, and further research on large-scale sample set reduction methods based on clustering.Starting with improving the difference of the base classifiers, we introduce random perturbations to further enhance the learning generalization ability in Adaboost and Bagging method. At last, we use weighted voting to design the integrated approach of the two level classifiers, and proposed a complete set of pattern recognition algorithm framework based on modular design and pipe ideology.
(6)Research of application of image recognition technology in medical diagnostics. We analyze the cell concentration levels obtained by experiments,and point out the diagnostic indicators of disease, provides a basis for PRRS diagnosis.
引文
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