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Identification of Myocardial Infarction Tissue Based on Texture Analysis From Echocardiography Images

机译:基于超声心动图图像纹理分析的心肌梗死组织识别

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摘要

Texture is an important characteristic that can be used for identification and detection for surface defect or abnormalities. This research has an algorithm for identifying heart with suspected myocardial infarction problem based on texture analysis applied on echocardiography images. Texture tissue sample images taken from echocardiography sub-image (ROI). There are two tissue classes: Type 1 corresponds to normal myocardial tissue, whereas Type 2 corresponds to infarcted myocardium with small dimension. Therefore, in order to investigate possible in differences tissue between patient with infarction tissue or not, we proposed a Wavelet Extension Transform and Gray Level Co-occurrence matrix.Wavelet Extension Transform is used to form an image approximation with higher resolution. The gray level co-occurrence matrices are computed for each sub-band. The feature vector of testing image and other feature vector as normal image classified by Mahalanobis distance to decide whether the test image is infarction or not. The method is tested with real data from echocardiography images of human heart. For each patient to be analyzed tissue samples are taken from not-affected area and tissue samples are taken from image segments corresponding to the infarcted area of myocardium. The result of this experiment can detect difference image from echocardiography as normal myocardium and infarcted myocardial tissue.
机译:质地是可用于识别和检测表面缺陷或异常的重要特征。这项研究提供了一种基于在超声心动图图像上应用的纹理分析来识别疑似心肌梗塞问题的心脏的算法。从超声心动图子图像(ROI)拍摄的纹理组织样本图像。有两种组织类型:1型对应于正常的心肌组织,2型对应于较小尺寸的梗塞心肌。因此,为了研究梗塞组织与非梗塞组织之间可能存在的组织差异,我们提出了小波扩展变换和灰度共生矩阵,利用小波扩展变换来形成具有更高分辨率的图像近似。针对每个子带计算灰度级共现矩阵。测试图像的特征向量和其他特征向量作为通过Mahalanobis距离分类的正常图像,以确定测试图像是否为梗塞。使用来自人心脏超声心动图图像的真实数据测试了该方法。对于每个要分析的患者,从未受影响的区域获取组织样本,并从与心肌梗塞区域相对应的图像段中获取组织样本。该实验的结果可以检测到作为正常心肌和梗塞心肌组织的超声心动图的差异图像。

著录项

  • 作者

    Agani, Nazori;

  • 作者单位
  • 年度 2008
  • 总页数
  • 原文格式 PDF
  • 正文语种 EN
  • 中图分类

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