A new and fast method for automatic tongue cancer image segmentation(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第8期
- Page:
- 1022-1029
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- A new and fast method for automatic tongue cancer image segmentation
- Author(s):
- CHEN Zhifeng1; DENG Xuan2; LU Zhentai2
- 1. Department of Stomatology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China 2. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Keywords:
- Keywords: tongue cancer image segmentation local mean-square error adaptive scale active contour model
- PACS:
- R318；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.08.016
- Abstract:
- Abstract: Objective To develop a novel method for automatic tongue cancer image segmentation, thereby realizing the quick and accurate segmentation of tongue tumor. Methods An adaptive scaling operator based on local mean-square error was introduced to realize the automatic adjustment of the evolution curve during the evolution process, thus making the curve move more efficiently to the real target boundary and overcoming the effects of bad factors such as unclear target boundary, edge blur and uneven gray level in tongue cancer image. In addition, in order to accelerate the convergence speed of the curve, a new energy term was proposed to evaluate the intensity distribution difference between the inner and outer regions of the evolution curve contour, so as to guide the curve to adjust the evolution speed adaptively and reduce the number of iterations required to complete the segmentation task. Results Twenty-two MRI images of tongue cancer were segmented by the proposed method. The overlap ratio (Dice value) between the segmented results and the real results was 0.82, and the Hausdorff distance (HD) was 1.732 mm. Conclusion The qualitative and quantitative comparisons between the proposed method and other existing active contour models reveals that the proposed algorithm is advantageous in the processing of details and weak edge gray level, and that it can be used for accurate segmentation of tongue tumor, thereby providing auxiliary information for clinical analysis.
Last Update: 2020-08-27