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Computer-aided method for automated image feature analysis and diagnosis of digitized medical images

机译:用于数字化医学图像的自动图像特征分析和诊断的计算机辅助方法

摘要

A computerized method for the detection and characterization of disease in an image derived from a chest radiograph, wherein an image in the chest radiograph is processed to determine the ribcage boundary, including lung top edges, right and left ribcage edges, and right and left hemidiaphragm edges. Texture measures including RMS variations of pixel values within regions of interest are converted to relative exposures and corrected for system noise existing in the system used to produce the image. Texture and/or geometric pattern indices are produced. A histogram(s) of the produced index (indices) is produced and values of the histograms) are applied as inputs to a trained artificial neural network, which classifies the image as normal or abnormal. In one embodiment, obviously normal and obviously abnormal images are determined based on the ratio of abnormal regions of interest to the total number of regions of interest in a rule-based method, so that only difficult cases to diagnose are applied to the artificial neural network.
机译:一种计算机化的方法,用于检测和表征从胸部X光片得出的图像中的疾病,其中对胸部X光片中的图像进行处理以确定胸腔边界,包括肺顶边缘,左右两侧的胸腔边缘以及左右侧mid肌边缘。包括感兴趣区域内像素值的RMS变化在内的纹理度量将转换为相对曝光,并针对用于生成图像的系统中存在的系统噪声进行校正。产生纹理和/或几何图案索引。产生所产生的索引的(多个)直方图,并将该直方图的值作为输入到经过训练的人工神经网络中,该人工神经网络将图像分类为正常还是异常。在一个实施例中,在基于规则的方法中,基于关注的异常区域与关注区域的总数之比,​​确定明显正常和明显异常的图像,从而仅将难以诊断的情况应用于人工神经网络。 。

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