Local segmentation of skull CT image using morphological processing and sparse field level set method

Xinyu Zhang; Guirong Weng; Yiming Ming

doi:10.1117/12.2502955

Translator Disclaimer

9 August 2018 Local segmentation of skull CT image using morphological processing and sparse field level set method

Xinyu Zhang, Guirong Weng, Yiming Ming

Author Affiliations +

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108062S (2018) https://doi.org/10.1117/12.2502955
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

How to quickly obtain the precise local region images of main structure within the skull from the whole set of computed tomography (CT) image is a significant job as well as a difficult task when analyzing the skull CT images. A local segmentation method of skull CT image based on morphological processing and sparse field level set is presented in this paper. First, using various morphological operations to remove the unnecessary regions and get the rough local image of target region. Then taking its contour as the initial evolution curve and utilizing the sparse field level set method to segment the skull CT image, the precise local region of main structures within the skull can be obtained, such as occipital bone which is prone to injury. Moreover, the target contour obtained by the previous segmentation can be used as the initial contour of the next image segmentation, because the adjacent slices image of CT are very similar. It helps to segment the whole set of CT image more quickly which is conducive to save a lot of time in clinical diagnosis. The experiment results show that the proposed method is feasible and has a great effect.

Citation Download Citation

Xinyu Zhang, Guirong Weng, and Yiming Ming "Local segmentation of skull CT image using morphological processing and sparse field level set method ", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062S (9 August 2018); https://doi.org/10.1117/12.2502955

ACCESS THE FULL ARTICLE