A non-rigid registration method for 3D CT images of mice
A non-rigid registration, CT image technology, applied in the field of medical image processing, can solve the problems of difficult to achieve manual segmentation of mouse atlas bones, long registration time, and complicated registration process, and achieve registration of three-dimensional images. Fast, reduced registration time, easy to achieve results
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Publication Date
- 2016-03-02
- Estimated Expiration
- Not applicable · inactive patent
Smart Images
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Abstract
Description
technical field
[0001] The invention relates to the field of medical image processing, and further relates to a registration method of medical images. The invention can be used for registration of three-dimensional CT images of mice. Background technique
[0002] Medical image registration plays an important role in medical image analysis and clinical experiments, and can be used in image segmentation, image fusion, surgical navigation and other technologies. In medical experiments, mice are often used as research objects, and image registration is necessary before analyzing and processing 3D images of mice. However, the body structure of mice is relatively complex, and there are often irregular non-rigid deformations between different images, and the degree of deformation of soft tissues and bones is different. It is necessary to treat soft tissues and bones differently during registration, otherwise it cannot Good alignment results were obtained. The traditional rigid r...
Examples
Embodiment Construction
[0057] Combine below figure 1 The specific implementation steps of the present invention are further described in detail.
[0058] Step 1. Rigid calibration of the source image
[0059] During rigid calibration, first correct the direction of the head and tail of the mouse in the image, then correct the supine posture of the mouse, and finally align the central axis of the mouse.
[0060] Step 2. Extract feature points of source image and target image
[0061] (1) Select the threshold, and use the threshold segmentation method to extract the bone of the mouse in the source image and the target image to obtain the source bone image and the target bone image. In this embodiment, the threshold value is 105;
[0062] (2) Calculate the area of each connected region in the target bone image perpendicular to the main axis of the mouse, calculate the centroid of the connected region with an area greater than 10, and obtain the feature points of the target image;
[0063] (3) Calc...