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SCM-motivated enhanced CV model for mass segmentation from coarse-to-fine in digital mammography

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Abstract

A novel approach for mass segmentation from coarse-to-fine in digital mammography, termed as SCM-motivated enhanced CV algorithm, is presented in this paper. As well known, it is difficult to robustly achieve mammogram mass segmentation due to low contrast between normal and lesion tissues, as well as high density tissue interference in mammograms. Therefore, Spiking Cortical Model with biology background is introduced to achieve mammary-specific and mass edge detection, and this mass candidate is regarded as the initial contour of improved CV model followed by, effectively overcoming the drawback that CV method is sensitive to the initial contour; especially, the enhanced CV model innovatively combines the techniques of physical imaging principle, and local region-scalable force, harvesting the coarse-to-fine mass boundary accurately. The proposed method is tested totally on 400 mammograms from two well-known digitized datasets (digital database for screening mammography and mammography image analysis society database), achieving the average detection rate of 93.25%. By comparing with the region-based model with bias field (Method 1) and typical CV model (Method 2), we can reach the conclusion that proposed method is outperform other methods, yielding the average sensitivity of 95.83%, specificity of 99.13%, dice similarity co-efficient of 92.21% and AUC of 98.02%. In addition, this method is verified on the mammograms from Gansu Provincial Cancer Hospital, the detection results reveal that our method can accurately detect the abnormal in clinical application.

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References

  1. Ali H, Badshah N, Chen K, Khan GA (2016) A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity. Pattern Recogn 51(C):27–42

    Article  Google Scholar 

  2. Berber T, Alpkocak A, Balci P, Dicle O (2013) Breast mass contour segmentation algorithm in digital mammograms. Comput Methods Prog Biomed 110(2):150–159

    Article  Google Scholar 

  3. Bresson X, Esedoglu S, Vandergheynst P, Thiran JP, Osher S (2007) Fast Global Minimization of the Active Contour/Snake Model. J Math Imaging Vision 28(2):151–167

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  Google Scholar 

  5. Chang X, Yang Y (2017) Semisupervised Feature Analysis by Mining Correlations Among Multiple Tasks. IEEE Trans Neural Netw Learn Syst 28(10):2294–2305

    Article  MathSciNet  Google Scholar 

  6. Chang X, Yu YL, Yang Y, Xing EP (2016) Semantic Pooling for Complex Event Analysis in Untrimmed Videos. IEEE Trans Pattern Anal Mach Intell 39(8):1617–1632

    Article  Google Scholar 

  7. de Oliveira Silva LC, Barros AK, Lopes MV (2017) Detecting masses in dense breast using independent component analysis. Artif Intell Med 80:29–38

    Article  Google Scholar 

  8. Dera D, Bouaynaya N, Fathallah-Shaykh HM (2016) Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI. Bull Math Biol 78(7):1–27

    Article  MathSciNet  MATH  Google Scholar 

  9. Eches O, Benediktsson JA, Dobigeon N, Tourneret JY (2010) Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral images. In: Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. p 1–4

  10. Edwards SD, Lipson JA, Ikeda DM, Lee JM (2013) Updates and revisions to the BI-RADS magnetic resonance imaging lexicon. Magn Reson Imaging Clin N Am 21(3):483–493

    Article  Google Scholar 

  11. Elter M, Held C, Wittenberg T (2010) Contour tracing for segmentation of mammographic masses. Phys Med Biol 55(55):5299–5315

    Article  Google Scholar 

  12. Faisal A, Pluempitiwiriyawej C (2012) Active contour using local region-scalable force with expandable kernel. In: IEEE International Conference on Information Science and Technology, Icist, p 18–24

  13. Gao X, Wang K, Guo Y, Yang Z, Ma Y (2015) Mass Segmentation in Mammograms Based on the Combination of the Spiking Cortical Model (SCM) and the Improved CV Model. In International Symposium on Visual Computing. Springer, Cham p 664–671

  14. Guo Y, Luo C, Ma Y (2017) Object detection system based on multimodel saliency maps. J Electron Imaging 26(2):023022

  15. Guo Y, Dong M, Yang Z, Gao X, Wang K, Luo C, Ma Y, Zhang J (2016) A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Comput Methods Prog Biomed 130:31–45

    Article  Google Scholar 

  16. Guo YN, Wang X, Yang Z, Wang D, Ma Y (2016) Improved Saliency Detection for Abnormalities in Mammograms. In: Computational Science and Computational Intelligence (CSCI), 2016 International Conference on. IEEE, p 786–791

  17. Guo Yn, Yang Z, Ma Y, Lian J, Zhu L (2017) Saliency Motivated Improved Simplified PCNN Model for object Segmentation. Neurocomputing 275:2179–2190

  18. Hsu WY (2012) Improved watershed transform for tumor segmentation: Application to mammogram image compression. Expert Syst Appl 39(4):3950–3955

    Article  Google Scholar 

  19. Jemal A, Bray F, Center MM, Ferlay JJ, Ward E, Forman D (2011) Global cancer statistics. CA Cancer J Clin 61(2):69–90

    Article  Google Scholar 

  20. Kim DH, Choi JY, Ro YM (2014) Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection. Comput Biol Med 63(C):238

    Google Scholar 

  21. Lan X, Ma AJ, Yuen PC (2014) Multi-cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation. In: Computer Vision and Pattern Recognition. p 1194–1201

  22. Lan X, Ma A, Yuen PC, Chellappa R (2015) Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking. IEEE Trans Image Process 24(12):5826

    Article  MathSciNet  Google Scholar 

  23. Lan X, Zhang S, Yuen PC (2016) Robust joint discriminative feature learning for visual tracking. In: International Joint Conference on Artificial Intelligence. p 3403–3410

  24. Lan X, Yuen PC, Chellappa R (2017) Robust MIL-Based Feature Template Learning for Object Tracking. In: AAAI. p 4118–4125

  25. Lankton S, Tannenbaum A (2008) Localizing region-based active contours.  IEEE Trans Image Process 17(11):2029–2039

    Article  MathSciNet  MATH  Google Scholar 

  26. 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  MATH  Google Scholar 

  27. Li C, Huang R, Ding Z, Gatenby J, Metaxas D, Gore J (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  MATH  Google Scholar 

  28. Li Z, Nie F, Chang X, Yang Y (2017) Beyond Trace Ratio: Weighted Harmonic Mean of Trace Ratios for Multiclass Discriminant Analysis. IEEE Trans Knowl Data Eng 29(10):2100–2110

    Article  Google Scholar 

  29. Liu J, Liu X, Chen J, Tang J (2011) Mass Segmentation in Mammograms Based on Improved Level Set and Watershed Algorithm. In International Conference on Intelligent Computing. p 502–508

  30. Liu CC, Tsai CY, Liu J, Yu CY, Yu SS (2012) A pectoral muscle segmentation algorithm for digital mammograms using Otsu thresholding and multiple regression analysis. Comp Math Appl 64(5):1100–1107

    Article  MATH  Google Scholar 

  31. Liu C, Liu W, Xing W (2017) An improved edge-based level set method combining local regional fitting information for noisy image segmentation. Signal Process 130:12–21

    Article  Google Scholar 

  32. Lu X, Dong M, Ma Y (2015) Automatic Mass Segmentation Method in Mammograms Based on Improved VFC Snake Model. In Emerging Trends in Image Processing, Computer Vision and Pattern Recognition. p 201–217

  33. Niu S, De SL, Chen Q, Leng T, Rubin DL (2016) Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor. Biomed Opt Express 7(2):581

    Article  Google Scholar 

  34. Noor B, Ke C, Haider A, Ghulam M (2012) Coefficient of Variation Based Image Selective Segmentation Model Using Active Contours. East Asian J Applied Math 2(2):150–169

    Article  MathSciNet  MATH  Google Scholar 

  35. Panetta K, Zhou Y, Agaian S, Jia H (2011) Nonlinear Unsharp Masking for Mammogram Enhancement. IEEE Trans Inf Technol Biomed 15(6):918–928

    Article  Google Scholar 

  36. Piovano J, Rousson M, Papadopoulo T (2007) Efficient segmentation of piecewise smooth images. In: International Conference on Scale Space and Variational Methods in Computer Vision. p 709–720

  37. Salmeri M, Mencattini A, Rabottino G, Accattatis A, Lojacono R (2009) Assisted Breast Cancer Diagnosis Environment: A Tool for DICOM mammographic images analysis. In: Medical Measurements and Applications, 2009. MeMeA 2009. IEEE International Workshop on. p 160–165

  38. Solem JE, Overgaard NC, Heyden A (2006) Initialization Techniques for Segmentation with the Chan-Vese Model. In: International Conference on Pattern Recognition. p 171–174

  39. Timp S, Varela C, Karssemeijer N (2007) Temporal change analysis for characterization of mass lesions in mammography. IEEE Trans Med Imaging 26(7):945–953

    Article  Google Scholar 

  40. Wang XF, Huang DS, Xu H (2010) An efficient local Chan-Vese model for image segmentation. Pattern Recognition. 43(3):603–618

  41. Wei K, Guangzhi W, Hui D (2006) Segmentation of the breast region in mammograms using watershed transformation. In: IEEE Engineering in Medicine and Biology Conference. p 6500–6503

  42. Wu Y, Hou W, Wu S (2011) Brain MRI segmentation using KFCM and Chan-Vese model. Trans Tianjin Univ 17(3):215–219

    Article  Google Scholar 

  43. Xia R, Liu W, Zhao J, Li L (2007) An Optimal Initialization Technique for Improving the Segmentation Performance of Chan-Vese Model. In: IEEE International Conference on Automation and Logistics. p 411–415

  44. Xie W, Li Y, Ma Y (2016) PCNN-based level set method of automatic mammographic image segmentation. Optik Int J Light Electron Opt 127(4):1644–1650

    Article  Google Scholar 

  45. Xu S, Liu H, Song E (2011) Marker-controlled watershed for lesion segmentation in mammograms. J Digit Imaging 24(5):754

    Article  Google Scholar 

  46. Yi-De MA, Yuan JX, Hong-Juan Z (2012) Self-Adaptive Method Using SCM for Noise Removal in Color Images. J Univ Electron Sci Technol China 41(5):754–758

    Google Scholar 

  47. Zhan K, Zhang H, Ma Y (2009) New spiking cortical model for invariant texture retrieval and image processing. IEEE Trans Neural Netw 20(12):1980

    Article  Google Scholar 

  48. Zhan K, Shi J, Wang H, Xie Y, Li Q (2017) Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review. Arch Comput Meth Eng 24(3):573–588

  49. Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: A survey of unsupervised methods. Comp Vis Image Underst 110(2):260–280

    Article  Google Scholar 

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Acknowledgements

Authors would like to thank the retrieval of all the public database for the experiments of this paper. This study was funded by the National Natural Science Foundation of China (nos.61175012 and 61201421) and Natural Science Foundation of Gansu Province (nos. 145RJZA181 and 1208RJZA265).

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China

    Ya’nan Guo, Xiaoli Gao, Zhen Yang, Jing Lian, Shiqiang Du, Huaiqing Zhang & Yide Ma

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  1. Ya’nan Guo
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  2. Xiaoli Gao
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  3. Zhen Yang
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  4. Jing Lian
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Correspondence to Yide Ma.

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Guo, Y., Gao, X., Yang, Z. et al. SCM-motivated enhanced CV model for mass segmentation from coarse-to-fine in digital mammography. Multimed Tools Appl 77, 24333–24352 (2018). https://doi.org/10.1007/s11042-018-5685-2

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