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首页> 外文会议>Advance Computing Conference (IACC) >Elastic registration of 2D abdominal CT images using hybrid feature point selection for liver lesions
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Elastic registration of 2D abdominal CT images using hybrid feature point selection for liver lesions

机译：使用混合特征点选择对肝脏腹部病变进行二维腹部CT图像的弹性配准

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Abdominal CT images have distinct intensity distribution. This feature is used to correct local deformations in the image. Reference and study images are decomposed using wavelet decomposition. Global deformations are first corrected applying rigid registration by use of maximization of mutual Information as the similarity measure at each level of registration hierarchy. Initially registered image and reference image are further elastically registered using landmark based elastic registration. Here landmarks or feature points are obtained by first intensity thresholding the images followed by boundary selection to obtain lesion boundaries and finally obtaining the centroid and convex hull points of lesions within the images. Convex hull points that lie on the boundary of lesions coupled with centroids of lesions are helpful in precisely identifying the lesions. An advantage of this is that lesions are enhanced to allow for deformations to be precisely determined. This is useful in improving diagnostic accuracy. The performance of algorithm is tested on a real case study of abdominal CT images with liver abscess. Considerable improvement in correlation coefficient and signal to noise ratio of the two images is observed.
机译：腹部CT图像具有明显的强度分布。此功能用于校正图像中的局部变形。参考和研究图像使用小波分解分解。首先，通过使用互信息的最大值作为配准层次的每个级别的相似性度量，通过应用刚性配准，对全局变形进行校正。使用基于界标的弹性配准进一步初始配准初始配准图像和参考图像。在这里，地标或特征点是通过首先对图像进行强度阈值处理，然后进行边界选择以获得病灶边界，最后获得图像内病灶的质心和凸包点而获得的。位于病灶边界上的凸壳点与病灶的质心一起有助于精确识别病灶。这样做的一个优点是，病变被增强以允许精确地确定变形。这对于提高诊断准确性很有用。该算法的性能在具有肝脓肿的腹部CT图像的真实案例研究中进行了测试。观察到两个图像的相关系数和信噪比有显着提高。

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