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1. 发明授权

US06549646B1 Divide-and-conquer method and system for the detection of lung nodule in radiological images 有权
标题翻译：用于放射学图像中肺结节检测的分治方法和系统
公开(公告)号：US06549646B1
公开(公告)日：2003-04-15
申请号：US09503840
申请日：2000-02-15
申请人： Hwa-Young Michael Yeh , Jyh-Shyan Lin , Yuan-Ming Fleming Lure , Xin-Wei Xu , Ruiping Li , Rong Feng Zhuang
发明人： Hwa-Young Michael Yeh , Jyh-Shyan Lin , Yuan-Ming Fleming Lure , Xin-Wei Xu , Ruiping Li , Rong Feng Zhuang
IPC分类号： G06K900
CPC分类号： G06K9/6814 , G06T7/0012 , G06T2207/30061
摘要： A divide-and-conquer (DAC) method and system improve the detection of abnormalities, like lung nodules, in radiological images via the use of zone-based digital image processing and artificial neural networks. The DAC method and system divide the lung zone into different zones in order to enhance the efficiency in detection. Different image enhancement techniques are used for each different zone to enhance nodule images, as are different zone-specific techniques for selecting suspected abnormalities, extracting image features corresponding to selected abnormalities, and classifying the abnormalities as either true or false abnormalities.
摘要翻译：一种分治（DAC）方法和系统通过使用基于区域的数字图像处理和人工神经网络来改善放射学图像中异常的检测，如肺结节。 DAC方法和系统将肺区分为不同的区域，以提高检测效率。每个不同区域都使用不同的图像增强技术来增强结节图像，以及用于选择可疑异常的不同区域特异性技术，提取对应于所选异常的图像特征，以及将异常分类为真或异常异常。

2. 发明授权

US06654728B1 Fuzzy logic based classification (FLBC) method for automated identification of nodules in radiological images 失效
标题翻译：基于模糊逻辑的分类（FLBC）方法，用于自动识别放射图像中的结节
公开(公告)号：US06654728B1
公开(公告)日：2003-11-25
申请号：US09625418
申请日：2000-07-25
申请人： Ruiping Li , Hwa-Young Michael Yeh , Yuan-Ming Fleming Lure , Xin-Wei Xu , Jyh-Shyan Lin
发明人： Ruiping Li , Hwa-Young Michael Yeh , Yuan-Ming Fleming Lure , Xin-Wei Xu , Jyh-Shyan Lin
IPC分类号： G06F1518
CPC分类号： G06T7/0012 , G06F19/321 , G06T2207/30061 , G16H50/20
摘要： A fuzzy logic based classification (FLBC) method for the automated discrimination of objects and the automated identification of nodules based on their features, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule (or, object) features are first normalized and then automatically selected. Based on the selected features, suspect nodules (or, objects) are pre-grouped and then subjected to the corresponding trained linear classifier to remove those false positive nodules or abnormal objects that are linearly separable. Finally, the remaining suspect nodules or objects are further subjected to a trained fuzzy classifier for removing those false positive nodules or abnormal objects that are not linearly separable.
摘要翻译：一种用于自动识别物体的模糊逻辑分类（FLBC）方法和基于其特征的结节的自动识别，用于实现该方法的程序化计算机，以及存储用于实现该方法的程序的存储介质，其中结节（或对象）特征首先被归一化，然后自动选择。基于选定的特征，可疑结节（或对象）被预先分组，然后经受相应训练的线性分类器以去除可线性分离的假阳性结节或异常物体。最后，剩余的嫌疑结节或物体进一步经受训练的模糊分类器，以去除不可线性分离的假阳性结节或异常物体。

4. 发明授权

US06760468B1 Method and system for the detection of lung nodule in radiological images using digital image processing and artificial neural network 失效
标题翻译：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
公开(公告)号：US06760468B1
公开(公告)日：2004-07-06
申请号：US09503839
申请日：2000-02-15
申请人： Hwa-Young Michael Yeh , Yuan-Ming Fleming Lure , Jyh-Shyan Lin
发明人： Hwa-Young Michael Yeh , Yuan-Ming Fleming Lure , Jyh-Shyan Lin
IPC分类号： G06K946
CPC分类号： G06T7/0012 , A61B6/583 , G06F19/00 , G16H50/20 , Y10S128/922 , Y10S128/925
摘要： 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.
摘要翻译：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。

5. 发明授权

US5857030A Automated method and system for digital image processing of radiologic images utilizing artificial neural networks 失效
标题翻译：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
公开(公告)号：US5857030A
公开(公告)日：1999-01-05
申请号：US629694
申请日：1996-04-09
申请人： Roger Stephen Gaborski , Yuan-Ming Fleming Lure , Thaddeus Francis Pawlicki
发明人： Roger Stephen Gaborski , Yuan-Ming Fleming Lure , Thaddeus Francis Pawlicki
IPC分类号： G06K9/62 , G06T5/00 , G06T7/00 , G06K9/80
CPC分类号： G06K9/62 , G06T7/0012 , G06T7/0081 , G06T7/0091 , G06T2207/10116 , G06T2207/20084 , G06T2207/30068 , G06T2207/30096
摘要： 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.
摘要翻译：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。

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