Abstract
The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford‐Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.
Highlights
Computed tomography (CT) has the advantages of fast imaging and high image resolution
On the basis of CT image superpixel gridding, based on the superpixel processing to improve the region growth criterion, the target edge of CT image is obtained and used as the initial contour to lay the foundation for accurate image segmentation [22, 23]
In order to verify the effectiveness of the algorithm proposed in this paper, a comparative experiment was designed to highlight the performance of this algorithm
Summary
Computed tomography (CT) has the advantages of fast imaging and high image resolution. In the computeraided diagnosis medical system, segmentation of the region of interest in the CT image is an important step in diagnosis and the key technical support for subsequent 3D image reconstruction. It plays an important role in the precise diagnosis and treatment of diseases and can reduce calculations to a certain extent [5]. With the help of the concept of superpixel segmentation, this paper proposes an improved segmentation algorithm for superpixel grid CT images using active contours. (2) The paper improves the Mumford-Shah model, fully considers the pixel texture characteristics, and increases the accuracy of CT image segmentation. The main contributions are as follows: (1) Using superpixels to improve the region growth criterion, the determination of the initial contour is more accurate. (2) The paper improves the Mumford-Shah model, fully considers the pixel texture characteristics, and increases the accuracy of CT image segmentation. (3)Various types of CT image data are used for experimental analysis, and the selection of experimental indicators is abundant, which greatly increases the validity of the experimental results
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