Liver Segmentation on Computed Tomography Images
Journal: Advances in Medicine and Engineering Interdisciplinary Research DOI: 10.32629/ameir.v1i3.1281
Abstract
Background: Liver segmentation using computed tomography data is the first step for the diagnosis of liver diseases. Currently, the segmentation of structures and organs is far from the precision level achieved by modern 3D systems based on images performed in the country's hospitals, so it is necessary to search for viable alternatives using the PDI on a computer. Objective: From a computational point of view, to determine an effective variant for the segmentation of liver images for clinical purposes in routine hospital conditions. Methods: Two modern segmentation methods (Graph Cut and EM/MPM) were compared by applying them to liver tomography images. An evaluative and statistical analysis of the results obtained in the segmentation of the images from the Dice, Vinet and Jaccard coefficients was carried out. Results: With the Graph Cut method, the desired region was segmented in all cases, and even when the image quality was low, a high similarity was observed between the segmented image and the reference mask. The level of visual detail is good, and edge reproduction remains true to the reference image. Image segmentation using the EM/MPM method was not always satisfactory. Conclusions: The Graph Cut segmentation method achieved a higher precision for the segmentation of liver images.
Keywords
computer-assisted image processing; X-ray computed tomography; liver
Full Text
PDF - Viewed/Downloaded: 0 TimesReferences
[1] Pham DL, Xu C, Prince JL. Arobe. Current Methods in Medical Image Segmentation. Annu Rev Biomed Eng. 2000; 2: 315-37.
[2] Sharma N, Aggarwal LM. Automated Medical Image Segmentation Techniques. J Med Phys. 2010; 35 (1): 3-14.
[3] Heimann T, Van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, et al. Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets. IEEE Trans Med Imaging. 2009; 28(8): 1251-65.
[4] Delong A, Osokin A, Isack HN, Boykov Y. Fast Approximate Energy Minimization with Label Costs. International Journal of Computer Vision. 2012; 96(1): 1-27.
[5] Chen Y, Zhao W, Wang ZJPIC. Level Set Segmentation Algorithm Based on Image Entropy and Simulated Annealing. In: 1st International Conference on Bioinformatics and Biomedical Engineering, 2007 [Internet]. Wuhan: IEEE; 2007. [ cited 7 Dic 2021 ] p. 999-1003. Available from: https://ieeexplore.ieee.org/document/4272743.
[6] Chen Y, Wang Z, Hu J, Zhao W, Wu Q. The Domain Knowledge Based Graph-cut Model for Liver CT Segmentation. Biomedical Signal Processing and Control. 2012; 7(6): 591-8.
[7] Comer ML, Delp EJ, editors. Parameter Estimation and Segmentation of Noisy or Textured Images Using the EM Algorithm and MPM Estimation. 1st International Conference on Image Processing, 1994 [Internet]. Austin: IEEE; 1994. [cited 7 Dic 2021] p. 650-54. Available from: https://ieeexplore.ieee.org/document/413651.
[8] Christ PF, Elshaer MEA, Ettlinger F, Tatavarty S, Bickel M, Bilic P, et al., editors. Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3d Conditional Random Fields. In: International Conference on Medical Image Computing and Computer-assisted Intervention, 2016 [Internet]. Ithaca: Cornell University; 2016. [cited Dic 12] Available from: https://arxiv.org/abs/1610.02177.
[9] Esneault S, Hraiech N, Delabrousse E, Dillenseger JL, editors. Graph Cut Liver Segmentation for Interstitial Ultrasound Therapy. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2007 [Internet]. New Jersey: IEEE; 2007. [cited 12 Dic 2021] p. 5247-50. Available from: https://ieeexplore.ieee.org/document/4353525.
[10] Massoptier L, Casciaro S, editors. Fully Automatic Liver Segmentation Through Graph-cut Technique. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2007 [Internet]. New Jersey: IEEE; 2007. [cited 12 Dic 2021] p. 5243-6. Available from: https://ieeexplore.ieee.org/abstract/document/43 53524.
[11] Masuda Y, Tateyama T, Xiong W, Zhou J, Wakamiya M, Kanasaki S, et al., editors. Liver Tumor Detection in CT Images by Adaptive Contrast Enhancement and the EM/MPM Algorithm. In: 8th IEEE International Conference on Image Processing; 2011 [Internet]. New Jersey: IEEE; 2007. [cited 12 Dic 2021] p. 1421-4. Available from: https://ieeexplore.ieee.org/document/6115708.
[2] Sharma N, Aggarwal LM. Automated Medical Image Segmentation Techniques. J Med Phys. 2010; 35 (1): 3-14.
[3] Heimann T, Van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, et al. Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets. IEEE Trans Med Imaging. 2009; 28(8): 1251-65.
[4] Delong A, Osokin A, Isack HN, Boykov Y. Fast Approximate Energy Minimization with Label Costs. International Journal of Computer Vision. 2012; 96(1): 1-27.
[5] Chen Y, Zhao W, Wang ZJPIC. Level Set Segmentation Algorithm Based on Image Entropy and Simulated Annealing. In: 1st International Conference on Bioinformatics and Biomedical Engineering, 2007 [Internet]. Wuhan: IEEE; 2007. [ cited 7 Dic 2021 ] p. 999-1003. Available from: https://ieeexplore.ieee.org/document/4272743.
[6] Chen Y, Wang Z, Hu J, Zhao W, Wu Q. The Domain Knowledge Based Graph-cut Model for Liver CT Segmentation. Biomedical Signal Processing and Control. 2012; 7(6): 591-8.
[7] Comer ML, Delp EJ, editors. Parameter Estimation and Segmentation of Noisy or Textured Images Using the EM Algorithm and MPM Estimation. 1st International Conference on Image Processing, 1994 [Internet]. Austin: IEEE; 1994. [cited 7 Dic 2021] p. 650-54. Available from: https://ieeexplore.ieee.org/document/413651.
[8] Christ PF, Elshaer MEA, Ettlinger F, Tatavarty S, Bickel M, Bilic P, et al., editors. Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3d Conditional Random Fields. In: International Conference on Medical Image Computing and Computer-assisted Intervention, 2016 [Internet]. Ithaca: Cornell University; 2016. [cited Dic 12] Available from: https://arxiv.org/abs/1610.02177.
[9] Esneault S, Hraiech N, Delabrousse E, Dillenseger JL, editors. Graph Cut Liver Segmentation for Interstitial Ultrasound Therapy. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2007 [Internet]. New Jersey: IEEE; 2007. [cited 12 Dic 2021] p. 5247-50. Available from: https://ieeexplore.ieee.org/document/4353525.
[10] Massoptier L, Casciaro S, editors. Fully Automatic Liver Segmentation Through Graph-cut Technique. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2007 [Internet]. New Jersey: IEEE; 2007. [cited 12 Dic 2021] p. 5243-6. Available from: https://ieeexplore.ieee.org/abstract/document/43 53524.
[11] Masuda Y, Tateyama T, Xiong W, Zhou J, Wakamiya M, Kanasaki S, et al., editors. Liver Tumor Detection in CT Images by Adaptive Contrast Enhancement and the EM/MPM Algorithm. In: 8th IEEE International Conference on Image Processing; 2011 [Internet]. New Jersey: IEEE; 2007. [cited 12 Dic 2021] p. 1421-4. Available from: https://ieeexplore.ieee.org/document/6115708.
Copyright © 2023 Melanie Yusta Gómez, Marlen Pérez Díaz, Rubén Orozco Morales, Xiomara Plasencia Hernández
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License