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Content Based Medical Image Retrieval for Histopathological, CT and MRI Images

机译:基于内容的组织病理学,CT和MRI图像医学图像检索

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A content based approach is followed for medical images. The purpose of this study is to access the stability of these methods for medical image retrieval. The methods used in color based retrieval for histopathological images are color co-occurrence matrix (CCM) and histogram with meta features. For texture based retrieval GLCM (gray level co-occurrence matrix) and local binary pattern (LBP) were used. For shape based retrieval canny edge detection and otsu‘s method with multivariable threshold were used. Texture and shape based retrieval were implemented using MRI (magnetic resonance images). The most remarkable characteristics of the article are its content based approach for each medical imaging modality. Our efforts were focused on the initial visual search. From our experiment, histogram with meta features in color based retrieval for histopathological images shows a precision of 60 % and recall of 30 %. Whereas GLCM in texture based retrieval for MRI images shows a precision of 70 % and recall of 20 %. Shape based retrieval for MRI images shows a precision of 50% and recall of 25 %. The retrieval results shows that this simple approach is successful.
机译:对于医学图像,遵循基于内容的方法。这项研究的目的是为了获得这些方法的医学图像检索的稳定性。在基于颜色的组织病理学图像检索中使用的方法是颜色共现矩阵(CCM)和具有元特征的直方图。对于基于纹理的检索,使用了GLCM(灰度共现矩阵)和局部二进制模式(LBP)。对于基于形状的检索,使用了Canny边缘检测和具有多变量阈值的otsu方法。使用MRI(磁共振图像)实现了基于纹理和形状的检索。本文最显着的特点是针对每种医学成像模式的基于内容的方法。我们的工作集中在最初的视觉搜索上。根据我们的实验,在基于颜色的组织病理学图像检索中具有元特征的直方图显示出60%的精度和30%的召回率。而GLCM在基于纹理的MRI图像检索中显示出70%的精度和20%的查全率。基于形状的MRI图像检索显示出50%的精度和25%的查全率。检索结果表明,这种简单的方法是成功的。

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