Method and device for realizing pulmonary nodule adaptive matching based on CT image skeleton registration

Abstract

The embodiment of the invention provides a method for realizing pulmonary nodule adaptive matching based on CT image skeleton registration. The method comprises the following steps of data preparation, lung and skeleton point cloud data extraction, three-dimensional point cloud data registration and pulmonary nodule adaptive matching. . Firstly, lung image data and pulmonary nodule data are subjected to three-dimensional point cloud rigid transformation registration based on the characteristic of small human skeleton change characteristics, so that alignment of the lung and pulmonary nodule data before and after follow-up visit is realized. Secondly, an FGR algorithm is adopted, so that the method is obviously superior to ICP and other local refinement algorithms in the aspects of operation speed and registration accuracy. Thirdly, RMSE is adopted as a lung point cloud registration error, adaptive matching of pulmonary nodules is achieved, manual intervention of pulmonary nodule registration is little, the automation degree is high, and the registration result is accurate; and fourthly, normalization processing is carried out on the CT image data, so that the robustness of the algorithm can be improved, and the method can be widely applied to different types of CT equipment and DICOM data with different pixel spacing values.

Description

technical field [0001] The invention relates to the technical field of medical equipment, in particular to a method and device for realizing adaptive matching of pulmonary nodules based on CT image skeleton registration. Background technique [0002] Lung cancer is one of the most harmful malignant tumors to human health and life today. Malignant pulmonary nodules are an important manifestation of early lung cancer. The growth characteristics of nodules reflect the relationship between the increase in the number or volume of cells in the nodules and time. With the rapid development of medical imaging and computer technology, computer-aided detection of pulmonary nodules based on CT images has become a research hotspot in the early diagnosis of lung cancer. Follow-up observation of CT images can effectively evaluate the growth characteristics of pulmonary nodules over a period of time , so as to provide a basis for early detection and accurate diagnosis of lung cancer. [0...

AI Technical Summary

Problems solved by technology

In actual testing, affected by factors such as patient position differences and respiration, the location and state of lung tissue before and after follow-up are often inconsistent, resulting in a large difference between CT images before and after follow-up

Due to the inconsistency of lung CT images and the unpredictability of nodule growth, the matching accuracy of the existing two types of matching methods is relatively low, and in severe cases, matching errors will occur

[0004] At the same time, the computational complexity of existing matching algorithms is high. With the continuous improvement of CT image quality, mainstream hardware platform configurations can no longer meet the needs of practical applications, and a matching algorithm with better performance is urgently needed.

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