Department of Electronics and Communications Engineering, RVS College of Engineering and Technology, Coimbatore, India.
Department of Biomedical Engineering, SGSITS, Indore, India.
Biomed Res Int. 2022 Aug 2;2022:6451770. doi: 10.1155/2022/6451770. eCollection 2022.
Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset.
全世界每天都有大多数人死于与肺癌有关的并发症。这是一种致命的疾病。为了提高一个人的生存机会，早期诊断是必要的前提。在这方面，现有的肿瘤检测方法，如 CT 扫描，最常用于识别感染区域。尽管如此，CT 成像仍然存在某些障碍，因此本文提出了一种新的模型，这是一种用于分析肺癌的基于相关性的模型。在使用滑杆运动对胸部和腹部器官的图像进行配准时，总变差正则项可以纠正边界不连续的位移场，但不能保持图像的局部特征，从而损失配准精度。通过像素点的空间位置权重对薄板样条能量算子和总变差算子进行空间加权，构建用于肺图像 CT 单模态配准和 CT/PET 双模态配准的自适应薄板样条总变差正则项。正则项与 CRMI 相似性度量和 L-BFGS 优化方法相结合，形成一种非刚性配准方法。根据 DIR-Lab 4D-CT 公共数据集和 CT/PET 临床数据集上的实验结果，该方法在保证图像内部平滑的同时，确保了边界的不连续运动，具有更高的配准精度。
