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Automatic segmentation of digital images applied in cardiac medical images

机译:在心脏病医学图像中应用数字图像的自动分割

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The digital image processing has been applied in several areas, especially where it is necessary use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this project aims to presents a adaptable segmentation method, that can be applied to different type of images, providing an better segmentation. The proposed method is based in a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. The technique was applied to segment the cell core and potential rejection of tissue in myocardial images of biopsies from cardiac transplant. The results are significant in comparison with those provided by one of the best known segmentation methods available in the literature.
机译:数字图像处理已应用于几个区域,特别是在必要时使用用于特征提取的工具并获得所研究的图像的模式。在初始阶段中,分割用于将图像分离为表示兴趣对象的部分,其可以在特定研究中使用。有几种意图执行此类任务,但很难找到一种可以容易地适应不同类型的图像的方法,这通常非常复杂或特定。为了解决这个问题,该项目旨在提出一种适应性的分割方法,可以应用于不同类型的图像,提供更好的分割。所提出的方法基于自动多级阈值的模型,并考虑组直方图量化的技术,分析直方图斜率百分比和最大熵的计算来定义阈值。施用该技术以在心脏移植心脏移植中分段细胞核心和潜在的心肌图像心肌图像中的组织。结果与文献中可用的最佳已知的分割方法之一提供的那些结果是显着的。

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