肾小球提取及球内细胞核的统计分析
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摘要
计算机技术和数字图像处理技术的不断发展,使得医学图像诊断在现代医疗中的地位越来越重要,现代医学已经越来越离不开医学图像所提供的信息,医学图像在临床诊断、教学科研等方面有着极其重要的作用。
本文针对肾脏组织切片图像中肾小球的提取及球内细胞核的统计分析对医学图像的识别技术进行了研究。所提出的肾小球组织的分割方法有三种,方法一,通过观察样本图像可知,肾囊壁内侧的空腔信息较肾囊壁信息强,因此将空腔定义为边界,定义复差分模板利用神经网络训练得到非线性阈值曲面对空腔边界进行增强,并融合传统意义上的肾囊壁边界,从而构造出完整的肾小球边界,达到提取目标物的目的;方法二,为了降低边界增强的难度,采用经典的边缘检测方法LOC滤波器增强边界,然后从寻找边界的角度出发,采用遗传算法在含有噪声的图像中搜索目标边界,从而提取目标;方法三,为保持真实边界的完整性,采用分水岭算法得到含有噪声的边界图像,结合遗传算法的搜索定位寻找肾小球组织的位置重心,将其设置为种子,利用区域生长法提取目标。
在成功提取肾小球组织后,对其内部的细胞核采用可变阈值与特征量反馈的方法进行二值化处理,利用模糊函数构造一个非线性阈值曲面,通过特征量反馈调节该阈值曲面直到自定义目标函数最优,得到最佳分割效果。对所得到的细胞核面积及数量进行统计,得到以面积为横坐标,以数量为纵坐标的统计分布图,计算均值及方差,给出病理数据。
本论文利用图像处理方法和智能算法对肾小球及球内细胞核的分割进行了研究,
西安理工大学硕士学位论文
实现了一个肾脏组织切片图像的自动处理系统。实验结果表明,该系统能够达到很好
的分割效果。
关键词:神经元网络,边界融合,遗传算法,分水岭算法,区域生长
With the rapid development of computer science and digital image technology, medical images diagnosis plays more and more important role in modern medical treatment. The information provided by medical image has been indispensable for modern medicine domain, and it also becomes significant in clinic diagnosis, scientific research and teaching aspects.The aim of this paper is to study the recognition techniques of medical images according to the glomerulus segmentation from kidney-tissue image and statistical analysis of nuclei. Three glomerulus segmentation methods are proposed in this paper: (1) From sample images, it can be seen that the information of cavum is much stronger than glomerulus edge, so cavum is defined as boundary which is enhanced with a nonlinear threshold surface constructed by neural network under typical user-defined feature template. After being syncretized with traditional boundary, the complete glomerulus boundary can be obtained and object will be extracted successfully. (2) To reduce the difficulty of boundary enhancement, LOG filter is applied to enhance glomerulus edge. From the point of view of boundary search, genetic algorithm is used to search boundary in the images containing noises and
accordingly glomerulus will be extracted. (3) To preserve the integrality of real boundary, watershed algorithm is applied. The barycenter of glomerulus found by genetic algorithm is used as the seed for region growing, and then the object will be obtained.As for nuclei in the glomerulus, variable thresholds and eigenvalue feedback strategy are used to get binary image firstly. A nonlinear threshold surface is constructed by Gaussian function. Then through adjusting this surface to optimize user-defined target function, the optimal segmentation results will be got. In terms of statistics, average value and variance can be calculated to present pathological data.In this paper, image processing methods and intelligent algorithm are used to segment glomerulus and nuclei, and an automatic analysis system of the kidney image is realized. The experimental result indicates the good performance of this system.
本文针对肾脏组织切片图像中肾小球的提取及球内细胞核的统计分析对医学图像的识别技术进行了研究。所提出的肾小球组织的分割方法有三种,方法一,通过观察样本图像可知,肾囊壁内侧的空腔信息较肾囊壁信息强,因此将空腔定义为边界,定义复差分模板利用神经网络训练得到非线性阈值曲面对空腔边界进行增强,并融合传统意义上的肾囊壁边界,从而构造出完整的肾小球边界,达到提取目标物的目的;方法二,为了降低边界增强的难度,采用经典的边缘检测方法LOC滤波器增强边界,然后从寻找边界的角度出发,采用遗传算法在含有噪声的图像中搜索目标边界,从而提取目标;方法三,为保持真实边界的完整性,采用分水岭算法得到含有噪声的边界图像,结合遗传算法的搜索定位寻找肾小球组织的位置重心,将其设置为种子,利用区域生长法提取目标。
在成功提取肾小球组织后,对其内部的细胞核采用可变阈值与特征量反馈的方法进行二值化处理,利用模糊函数构造一个非线性阈值曲面,通过特征量反馈调节该阈值曲面直到自定义目标函数最优,得到最佳分割效果。对所得到的细胞核面积及数量进行统计,得到以面积为横坐标,以数量为纵坐标的统计分布图,计算均值及方差,给出病理数据。
本论文利用图像处理方法和智能算法对肾小球及球内细胞核的分割进行了研究,
西安理工大学硕士学位论文
实现了一个肾脏组织切片图像的自动处理系统。实验结果表明,该系统能够达到很好
的分割效果。
关键词:神经元网络,边界融合,遗传算法,分水岭算法,区域生长
With the rapid development of computer science and digital image technology, medical images diagnosis plays more and more important role in modern medical treatment. The information provided by medical image has been indispensable for modern medicine domain, and it also becomes significant in clinic diagnosis, scientific research and teaching aspects.The aim of this paper is to study the recognition techniques of medical images according to the glomerulus segmentation from kidney-tissue image and statistical analysis of nuclei. Three glomerulus segmentation methods are proposed in this paper: (1) From sample images, it can be seen that the information of cavum is much stronger than glomerulus edge, so cavum is defined as boundary which is enhanced with a nonlinear threshold surface constructed by neural network under typical user-defined feature template. After being syncretized with traditional boundary, the complete glomerulus boundary can be obtained and object will be extracted successfully. (2) To reduce the difficulty of boundary enhancement, LOG filter is applied to enhance glomerulus edge. From the point of view of boundary search, genetic algorithm is used to search boundary in the images containing noises and
accordingly glomerulus will be extracted. (3) To preserve the integrality of real boundary, watershed algorithm is applied. The barycenter of glomerulus found by genetic algorithm is used as the seed for region growing, and then the object will be obtained.As for nuclei in the glomerulus, variable thresholds and eigenvalue feedback strategy are used to get binary image firstly. A nonlinear threshold surface is constructed by Gaussian function. Then through adjusting this surface to optimize user-defined target function, the optimal segmentation results will be got. In terms of statistics, average value and variance can be calculated to present pathological data.In this paper, image processing methods and intelligent algorithm are used to segment glomerulus and nuclei, and an automatic analysis system of the kidney image is realized. The experimental result indicates the good performance of this system.
引文
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[3] 刘志红,黎磊石.迈向21世纪的中国肾脏病临床.肾脏病与透析肾移植杂志,10(1),2001:1-2.
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[5] 胡晓莉.做好肾活检组织制片技术的几点体会.内蒙古医学院学报,20(4),1998:283-284.
[6] 许朝晖毕业设计论文.医学图像识别技术的研究.2003.
[7] 张小牤博士论文.医学图像的领域分割法.1996.
[8] 朱虹博士论文.肾脏组织图像的解析方法的研究.1999.
[9] 郑南宁.计算机视觉与模式识别.北京:国防工业出版社,1998.3.
[10] 谷口庆治编,朱虹等译.数字图像处理—应用篇.北京:科学出版社,2002.7.
[11] 杨枝灵,王开等.数字图像处理获取、处理及实践应用.2003:504-529.
[12] 袁华,滕奇志,袁支润,陶德元.边缘检测及其在医学图像中的应用.生物医学工程学杂志,18(1).2001:149-153.
[13] 袁华,吴效明,岑人经等.滤除血管减影图像噪声的多SE形态金字塔法.暨南大学学报(自然科学版),21(1),2000:65-67.
[14] 韩超,陈君楷,袁支潤等.造影血管边缘的B-立方样条平滑变换的多尺度检测方法.生物医学工程学杂志,13(3),1996:235-238。
[15] Dong Joong Kang. A Fast and Stable Snake Algorithm for Medical Images. Pattern Recognition Letters, 20, 1999: 507-512.
[16] 赵巨波,孙华燕,杜巍.一种图像边缘特征提取算法.光学精密工程,8(4),2000:325—327.
[17] Fan JP, Yau DKY, Elmagarmid AK et al. Automatic Image Segmentation by Integrating Color-edge Extraction and Seeded Region Growing. IEEE Transactions on Image Processing, 10(10), 2001: 1454—1461.
[18] Tsai CM, Lee HJ, Binarization of Color Document Images via Luminance and Saturation Color Features. IEEE Transactions on Image Processing, 11(4), 2002: 434—439.
[19] Zhu H, Taniguchi K, Watanabe SZ, et al. Extraction of Renal Glomerular Regions Using a Non-linear Difference Filter and Region-estimation Model. MEDICAL IMAGING TECHNOLOGY. 17(6), 1999: 669—773.
[20] 张军,朱虹,许朝晖等.神经元网络容错学习在肾小球区域边界增强中的应用.生物医学工程学报,录用。
[21] 马东,曹培杰,李斌.多信息融合的彩色细胞图像分割方法,北京生物医学工程.19(1),2000:33-38.
[22] 王月兰,曾迎生.信息融合技术在彩色图像分割方法中的应用,计算机学报.23(7),2002:763-767.
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