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    • 2. 发明授权
    • Automated method and system for digital image processing of radiologic
images utilizing artificial neural networks
    • 使用人工神经网络的放射图像数字图像处理的自动化方法和系统
    • US5857030A
    • 1999-01-05
    • US629694
    • 1996-04-09
    • Roger Stephen GaborskiYuan-Ming Fleming LureThaddeus Francis Pawlicki
    • Roger Stephen GaborskiYuan-Ming Fleming LureThaddeus Francis Pawlicki
    • G06K9/62G06T5/00G06T7/00G06K9/80
    • G06K9/62G06T7/0012G06T7/0081G06T7/0091G06T2207/10116G06T2207/20084G06T2207/30068G06T2207/30096
    • An automated method and system for digital imaging processing of radiologic images, wherein digital image data is acquired and subjected to multiple phases of digital imaging processing. During the Pre-Processing stage, simultaneous box-rim filtering and k-nearest neighbor processing and subsequent global thresholding are performed on the image data to enhance object-to-background contrast, merge subclusters and determine gray scale thresholds for further processing. Next, during the Preliminary Selection phase, body part segmentation, morphological erosion processing, connected component analysis and image block segmentation occurs to subtract unwanted image data preliminarily identify potentials areas including abnormalities. During the Pattern Classification phase, feature patterns are developed for each area of interest, a supervised, back propagation neural network is trained, a feed forward neural network is developed and employed to detect true and several false positive categories, and two types of pruned neural networks are utilized in connection with a heuristic decision tree to finally determine whether the regions of interest are abnormalities or false positives.
    • 一种用于放射图像的数字成像处理的自动化方法和系统,其中获取数字图像数据并进行数字成像处理的多个阶段。 在预处理阶段期间,对图像数据执行同时的盒边缘滤波和k个最近邻处理以及随后的全局阈值处理,以增强对象对背景对比度,合并子集群并确定用于进一步处理的灰度阈值。 接下来,在初步选择阶段,进行身体部位分割,形态侵蚀处理,连通分量分析和图像块分割,以减去不需要的图像数据,初步识别包括异常的电位区域。 在模式分类阶段,针对感兴趣的每个区域开发特征模式,对受监督的反向传播神经网络进行了训练,开发了一种前馈神经网络,用于检测真假和正误分类,以及两种类型的修剪神经 网络利用启发式决策树来最终确定感兴趣的区域是异常还是误报。
    • 10. 发明授权
    • Method and system for the detection of lung nodule in radiological images using digital image processing and artificial neural network
    • 使用数字图像处理和人工神经网络在放射学图像中检测肺结节的方法和系统
    • US06760468B1
    • 2004-07-06
    • US09503839
    • 2000-02-15
    • Hwa-Young Michael YehYuan-Ming Fleming LureJyh-Shyan Lin
    • Hwa-Young Michael YehYuan-Ming Fleming LureJyh-Shyan Lin
    • G06K946
    • G06T7/0012A61B6/583G06F19/00G16H50/20Y10S128/922Y10S128/925
    • A method and system improve the detection of abnormalities, such as lung nodules, in radiological images using digital image processing and artificial neural network techniques. The detection method and system use a nodule phantom for matching in order to enhance the efficiency in detection. The detection method and system use spherical parameters to characterize true nodules, thus enabling detection of the nodules in the mediastinum. The detection method and system use a multi-layer back-propagation neural network architecture not only for the classification of lung nodules but also for the integration of detection results from different classifiers. In addition, this method and system improve the detection efficiency by recommending the ranking of true nodules and several false positive nodules prior to the training of the neural network classifier. The method and system use image segmentation to remove regions outside the chest in order to reduce the false positives outside the chest region.
    • 一种方法和系统通过数字图像处理和人工神经网络技术改善放射学图像异常检测,如肺结节。 检测方法和系统使用结节体模进行匹配,以提高检测效率。 检测方法和系统使用球形参数来表征真实结节,从而能够检测纵膈中的结节。 检测方法和系统使用多层反向传播神经网络结构,不仅用于肺结节的分类,而且用于整合来自不同分类器的检测结果。 此外,该方法和系统通过在神经网络分类器的训练之前推荐真实结节和几个假阳性结节的排名来提高检测效率。 该方法和系统使用图像分割去除胸部以外的区域,以减少胸部以外的假阳性。