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Active contour model based on local intensity fitting and atlas correcting information for medical image segmentation

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Abstract

Intensity inhomogeneity and noises often occur in real medical images, which present a large degree of challenge to image segmentation. At the same time, most of the existing image segmentation algorithms are sensitive to initial conditions and model parameters. This paper presents an accurate and robust active contour model to solve the above problems. Inspired by the idea of the region-scalable fitting (RSF) model, we first define a local atlas fitting term transformed by the segmentation contour of the coherent local intensity clustering (CLIC) model. Then, we define a new energy functional by merging the atlas term into the energy functional of the RSF model. The advantage of this operation is that it makes full use of the existing segmentation features and advantages of the two models and avoids cumbersome adjustment of model parameters and initial contours. The experimental results clearly show that the improved model not only has better segmentation results than the RSF model and other active contour models such as the LINC, REGAC and SMAP models, but also solves the problem of sensitivity to initial contours, parameters adjustment and noise.

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References

  1. Bustamante M, Petersson S, Eriksson J, Alehagen U, Dyverfeldt P, Carlhall CJ, Ebbers T (2015) Atlas-based analysis of 4d flow CMR: Automated vessel segmentation and flow quantification. J Cardiovasc Magn Reson 17 (1):87

    Article  Google Scholar 

  2. Caselles V, Kinmmel R, Sapiro G (1997) Geodesic active contour models. Int J Comput Vis 22(1):61–79

    Article  Google Scholar 

  3. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  Google Scholar 

  4. Cheng J, Foo SW (2006) Markovian level set for echocardiographic image segmentation. In: 2006 IEEE International Symposium on Circuits and Systems, pp 5567–5570

  5. Gong Z, Lu Z, Zhao D, Wang S, Liu Y, Song Y, Xuan K, Tan W, Li C (2017) Level set framework of multi-atlas label fusion with applications to magnetic resonance imaging segmentation of brain region of interests and cardiac left ventricles. Digit Med 3:76

    Article  Google Scholar 

  6. Jaccard P (1912) The distribution of flora in the alpine zone. Phytol 11(2):37–50

    Article  Google Scholar 

  7. Karasawa K, Oda M, Kitasaka T, Misawa K, Fujiwara M, Chu C, Zheng G, Rueckert D, Mori K (2017) Multi-atlas pancreas segmentation: atlas selection based on vessel structure. Med Image Anal 39:18–28

    Article  Google Scholar 

  8. Kass M, Witkin A, Terzopoulos D (1988) Snakes: Active contour models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

  9. Li C, Huang R, Ding Z, Gatenby JC, Metaxas DN (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2016

    Article  MathSciNet  Google Scholar 

  10. Li C, Kao CY, Gore JC, Ding Z (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process 17 (10):1940–1949

    Article  MathSciNet  Google Scholar 

  11. Li C, Xu C, Anderson AW, Gore JC (2009) MRI Tissue classification and bias field estimation based on coherent local intensity clustering: a unified energy minimization framework. In: Information processing in medical imaging . Springer, pp 288–299

  12. Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19 (12):3243–3254

    Article  MathSciNet  Google Scholar 

  13. Lim KY, Mandava R (2018) A multi-phase semi-automatic approach for multisequence brain tumor image segmentation. Expert Syst Appl 112:288–300

    Article  Google Scholar 

  14. Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pur Appl Math 42(5):577–685

    Article  MathSciNet  Google Scholar 

  15. Pan Y, He F, Yu H (2018) A novel enhanced collaborative autoencoder with knowledge distillation for top-N recommender systems. Neurocomputing 332:137–148

    Article  Google Scholar 

  16. Pan Y, He F, Yu H (2020) A correlative denoising autoencoder to model social influence for top-N recommender system. Front Comput Sci 14(3):143–301

    Article  Google Scholar 

  17. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, pp 234–241

  18. Tanzi L, Vezzetti E, Moreno R, Moos S (2020) X-ray bone fracture classification using deep learning: A baseline for designing a reliable approach. Appl Sci 10(4):2076–3417

    Article  Google Scholar 

  19. Tashk A, Herp J, Nadimi E (2019) Automatic segmentation of colorectal polyps based on a novel and innovative convolutional neural network approach. Intensive Care Med 14:384–91

    Google Scholar 

  20. Tashk A, Hopp T, Ruiter NV (2019) An innovative practical automatic segmentation of ultrasound computer tomography images acquired from usct system. Iran Jo Sci Technol Trans Electr Eng 43(2):167–180

    Article  Google Scholar 

  21. Tashk A, Nadimi E (2020) An innovative polyp detection method from colon capsule endoscopy images based on a novel combination of RCNN and DRLSE. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp 1–6. Glasgow, UK

  22. Tor-Díez C, Passat N, Bloch I, Faisan S, Bednarek N, Rousseau F (2018) An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI. Comput Med Imaging Graph 70:73–82

  23. Vese LA, Chan TF (2002) A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vis 50(3):271–293

    Article  Google Scholar 

  24. Wang L, Chang Y, Wang H, Wu Z, Pu J, Yang X (2017) An active contour model based on local fitted images for image segmentation. Inf Sci 418–419:61–73

  25. Yang H, Liu P, She Y, Liu D, Guo D (2013) Ultrasonic imaging contrast enhancement using modified dehaze image model. Electron Lett 49(19):1209–1211

  26. Yu H, He F, Pan Y (2018) A novel region-based active contour model via local patch similarity measure for image segmentation. Multimed Tools Appl 77(18):24097–24119

    Article  Google Scholar 

  27. Yu H, He F, Pan Y (2019) A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimed Tools Appl 78(9):11779–11798

    Article  Google Scholar 

  28. Yu H, He F, Pan Y (2020) A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation. Multimed Tools Appl 79:5743–5765

    Article  Google Scholar 

  29. Zhang J, He F, Chen Y (2020) A new haze removal approach for sky/river alike scenes based on external and internal clues. Multimed Tools Appl 79 (3):2085–2107

    Article  Google Scholar 

  30. Zhang S, He F, Ren W, Yao J (2020) Joint learning of image detail and transmission map for single image dehazing. Vis Comput 36(2):305–316

    Article  Google Scholar 

  31. Zhang K, Song H, Zhang L (2010) Active contours driven by local image fitting energy. Pattern Recogn 43(4):1199–1206

    Article  Google Scholar 

  32. Zhang W, Wang X, Zhang P, Junfeng C (2017) Global optimal hybrid geometric active contour for automated lung segmentation on CT images. Comput Biol Med 91:168–180

    Article  Google Scholar 

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Authors and Affiliations

  1. Harbin Institute of Technology (Shenzhen), HIT Campus of Shenzhen University Town, G710, Xili, Shenzhen, 518055, China

    Yunyun Yang, Ruofan Wang & Huilin Ren

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  1. Yunyun Yang
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  2. Ruofan Wang
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  3. Huilin Ren
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Correspondence to Yunyun Yang.

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Yang, Y., Wang, R. & Ren, H. Active contour model based on local intensity fitting and atlas correcting information for medical image segmentation. Multimed Tools Appl 80, 26493–26509 (2021). https://doi.org/10.1007/s11042-021-10890-4

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  • DOI: https://doi.org/10.1007/s11042-021-10890-4

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