Recognition of the pulmonary vessels by knowledge base of the radiologic anatomy of the bronchopulmonary tree for the computer diagnosis of the tumor of the lung.

在胸部X线或胸部CT上肺周围结节状阴影的自动诊断系统中，假阳性阴影的判别是一个重要问题，在计算机图像分析系统的图像识别研究中，癌结节状阴影显示出与胸部X线血管阴影相似的输出值。通过使用专门设计的提取滤波器和在适当阈值水平下消除的分析技术来识别这些图像是困难的。本研究的目的是建立基于三维解剖学的医学图像知识库，建立肺结节的检测方法和假阳性阴影的消除方法。[方法与结果]1）基于三维解剖学的图像知识库建立了一个包含血管长度和方向信息的解剖学知识库。它包括从种脐到外周的信息， ...更多信息 选择血管的原始分支模式。作为知识库的模型，本研究使用了Yamashita创建的流行模式组合。将右主支气管的长度设定为3个单位，从而确定血管的长度。方向被三维地展示为26个方向之一。2）基于解剖学知识库的血管决策建立了基于具有长度、方向和连续性信息的解剖学知识库的血管决策算法。3）肺结节自动检测系统利用胸部螺旋CT图像，研制了一套肺结节自动检测系统。为了检测CT图像上的结节，我们使用针对胸部X线片开发的结节方向对比度滤波器（DCF-N），并针对CT图像修改其参数。87.5%的结节被该系统成功检出，但该系统也检出了许多假阳性病灶。包括血管解剖结构的信息，它将有可能减少假阳性和提高该系统的鉴别诊断价值。[结论]该系统对血管的识别能力可能不足以满足未来的使用。这种情况的原因之一可能是由于对应于动脉树的多个分支模式模型的信息不足。根据长度、方向和连续性的信息很难区分肺动脉和静脉。必须在系统中定义血管的叶位置信息。对于结节检测，该系统可以很好地检测到具有三个同心圆结构的结节性混浊。少

Discrimination of false positive shadow is important in an automatic diagnosis system for nodular shadow of the peripheral lung on chest radiogram or on chest CT.In an image recognition study by computed analysis system, nodular shadow of the cancer revealed an output value that looks like those of the shadows of the blood vessels on the chest radiogram. It is difficult to identify those images by analytical technique using specially designed extraction filter and elimination at an appropriate threshold level. Construction of the knowledge database of medical images based on the 3-dimensional anatomy is an aim of this study to establish a detection method for pulmonary nodules and an elimination method for false positive shadows. [Methods and Results]1)Kowledge database of images based on the 3-dimensional anatomyAn anatomical knowledge base which has length and information of direction of blood vessels was originally created. It includes information from the hilum to the periphery ref … More lecting original branching pattern of the blood vessels. As a model of knowledge base, a combination of prevalence patterns that created by Yamashita was used in this study. Length of the vessels were decided as the length of the right main bronchus was set to three units. Directions was 3-dimensionally demonstrated as one of 26 directions. Continuity of the vessels was shown as parent-children-grandchildren relationships.2)Decision of blood vessels on the basis of anatomical knowledge baseDecision algorithm of the blood vessels on the basis of anatomical knowledge base which has information of length, direction, and continuity was created. However, it was impossible to name vessels on only this anatomical knowledge database.3)Automated detection system for pulmonary nodulesUsing chest helical CT image, an automated detection system for pulmonary nodules has been developed. To detect nodules on CT images, we use the directional contrast filter for nodule (DCF-N) which was developed for chest radiograms and its parameters were modified for CT images. Nodules of 87.5% was successfully detected by this system, however, many falsepositive foci was detected by this system. Including information of vascular anatomy, it will be possible to reduce false-positives and improve the differential diagnostic value of this system. [Conclusions]The ability of this system to recognition blood vessels may not be sufficient for future use. One of the reason of this situation might be due to insufficient information that correspond to multiple branching pattern models of the arterial tree. There was a difficulty to differentiate pulmonary arteries from veins by the information of length, direction, and continuity. Information of lobar position of the vessels must be defined in the system. As for nodule detection, the system fairly detected nodular opacities which have three-concentric circular structures. Less