首页> 外文会议>Information Management and Engineering, 2009. ICIME '09 >Segmenting Extra Pulmonary Tuberculous Lesions in Computed Tomography Images Using Positron Emission Tomography Intensity Markers
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Segmenting Extra Pulmonary Tuberculous Lesions in Computed Tomography Images Using Positron Emission Tomography Intensity Markers

机译:使用正电子发射断层扫描强度标记在计算机断层扫描图像中分割额外的肺结核病变

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Various studies have been conducted to expand the utilization of combined positron emission tomography and computed tomography (PET/CT) covering cases of infection and inflammation. PET images provide the functional activity of a lesion while CT images demonstrate the anatomical location. Hence, existence of infected lesions can be recognized in PET image but since the structural position can not be precisely defined on PET images, we need to retrieve this information from CT. We highlight localization of extra pulmonary tuberculosis infection using high activity points on PET image as references to extract regions of interest on CT image. Once PET and CT images have been registered, coordinates of the candidate points on PET are fed into seeded region growing algorithm to define the boundary of lesion on CT. The region growing process continues until a significant change in bilinear pixel values is reached. Results show that this algorithm works well considering the limitations of seeded region growing algorithm.
机译:已经进行了各种研究以扩大结合正电子发射断层扫描和计算机断层扫描(PET / CT)的利用,从而涵盖感染和炎症的情况。 PET图像可提供病变的功能活动,而CT图像可显示解剖位置。因此,可以在PET图像中识别出感染病灶的存在,但是由于无法在PET图像上精确定义结构位置,因此我们需要从CT检索此信息。我们突出显示额外的肺结核感染的本地化,使用PET图像上的高活性点作为提取CT图像上感兴趣区域的参考。一旦PET和CT图像已配准,就将PET上候选点的坐标输入到播种区域生长算法中,以定义CT上病变的边界。区域生长过程一直持续到双线性像素值发生显着变化为止。结果表明,考虑到种子区域生长算法的局限性,该算法工作良好。

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