【作者】 李春燕；

【导师】 房斌；

【作者基本信息】 重庆大学 ， 计算机应用技术， 2010， 硕士

【摘要】 自动尿沉渣分类仪器对于临床尿检具有重要的意义。它解决了长期以来传统的通过人工对尿沉渣涂片进行目镜检查的操作过程中存在的受技术水平,视觉上的偏差影响,工作效率低,无法给出定量分析结果的弊端,把医务人员从繁重的重复性劳动中解放出来。排除各种由于主观因素而产生的误差,实现对图像的快速精确定量分析,提高诊断的准确性。同时,数字化的检验结果和图样可以方便的实现图像,数据的远程调用,为远程医疗与疾病的会诊等工作提供便利。本文在数字图像处理与模式识别的理论基础上,通过深入的研究和大量的实验,提出了一个针对复杂尿沉渣显微图像中各种有形成分分割定位于两类有成成分即精子,管型细胞识别的系统方法。本系统能够将数字显微图像中可以为诊断提供依据的各类有形成分:白细胞,红细胞,上皮细胞,管型,精子,结晶,霉菌等从背景中很好的分割出来,并通过决策树分类器识别出其中的管型细胞和精子。首先,在图像分割方面,本文充分利用8方向灰度信息平衡光照不均等因素,得到标准差梯度图像,采用基于标准差梯度图的直方图统计信息的双阈值分割算法对图像进行二值化,并对初步分割后的图像根据局部灰度图像灰度均一性强度有选择性的选取canny操作的阈值,随后基于canny图,通过形态学闭操作和5X5邻域的图像边缘链接操作,对初步二值图像进行紧致。最后针对对多细胞粘连,细胞团的情况,基于距离图与部分先验知识进行多细胞分割,定位细胞团中的每个细胞。在特征选取方面,利用管型细胞均匀圆柱体,精子头尾两部分及尾部呈粗细均匀的管状结构等典型特征分别设计提取了多个形态特征参数,并对管型设计了简单的纹理特征。在分类器设计上面,为了提高识别器的效率,降低其复杂度,选取了基于二叉决策树的分类器,结合形态特征和纹理特征做为分类属性,识别两类有形成分。经临床大量尿液样本验证表明,本文设计的系统使得尿沉渣图像中各有形成分分割完整,定位准确,为下一步识别垫定了良好的基础。在粗筛定位上,管型和精子的识别率也达到了较好的水平。更多还原

【Abstract】 Automatic urine sediment classifier has significant influence on clinic urine analy-sis. As compared to the traditional manual way which is limited by the technical level, deviation error based on vision and low efficience, it can relieve the doctors of their hard, time consuming manual work and avoid diagnostic error caused by subjectivism. Moreover, it provides quantitative analysis and high efficience. Meanwhile, digital re-sults and pictures are convient for long-distance transfer which is important for long-distance medical treatment and consultation.Base on theories of digital image processing and pattern recogniton and some ex-periment, we develop a strategy for objects detection and recognition of two of them, which are cast and sperm. Our system is able to segment various sediments which are useful for diagnostic analysis from the backgroud of digital microscopic image. These sediments include white blood cell, red blood cell, epithclium, cast, sperm, crystal and mycete. The classifier of system can recognize cast and sperm from other objects.In image segmentation stage, first of all, based on the statistic information of stan-dard difference gradient image’s histogram, we employ an bi-thresholding method. Second, a refining strategy adopting the combined canny and gray image information is selectivly applied to some lacal part of the previous binary image.At last, in terms of cell mass, in the light of priori knowledge of blood cells, we exlpore a method making use of distantce transform image information to locate each of the blood cells in the mass and thereby segment them from the mass.In feature selcetion, we extract some shape features according cast and sperm’s physic characteristics. As for cast, we additionally design a texture feature.In classifier designing, classifiers based on dicision tree are developed for high ef-ficiency and simpleness. The classifier for cast combines shape features with texture features.A large number experimental results show that our strategy can extract objects from background neatly and precisely. Meanwhile, it can also achieve satisfactort recognition rates.更多还原