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A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography

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Abstract

Architectural distortion (AD) is the earliest sign of breast cancer that can be detected on a mammogram, and it is usually associated with malignant tumors. Breast cancer is one of the major causes of death among women, and the chance of cure can increase significantly when detected early. Computer-aided detection (CAD) systems have been used in clinical practice to assist radiologists with the task of detecting breast lesions. However, due to the complexity and subtlety of AD, its detection is still a challenge, even with the assistance of CAD. Recently, the fusion of descriptors has become a trend for improving the performance of computer vision algorithms. In this work, we evaluated some local texture descriptors and their possible combinations, considering different fusion approaches, for application in CAD systems to improve AD detection. In addition, we present a novel fusion-based texture descriptor, the Completed Mean Local Mapped Pattern (CMLMP), which is based on complementary information between three LMP operators (signal, magnitude and center) and the local differences between pixel values and the mean value of a neighborhood. We compared the performance of the proposed descriptor with two other well-known descriptors: the Completed Local Binary Pattern (CLBP) and the Completed Local Mapped Pattern (CLMP), for the task of detecting AD in 350 digital mammography clinical images. The results showed that the descriptor proposed in this work outperforms the others, for both individual and fused approaches. Moreover, the choice of the fusion operator is crucial because it results in different detection performances.

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References

  1. WHO, ”World health organization - breast cancer,” 2019. [Online]. Available: http://www.who.int/

  2. Elmore JG, Nakano CY, Koepsell TD, et al: International variation in screening mamography interpretations in community-based programs. J Natl Cancer Inst 95:(18)1384-1393, 2003

    Article  Google Scholar 

  3. Veronesi U, Boyle P, Goldhirsch A, et al: Breast Cancer. The Lancet 365:1727–1741, 2005

    Article  Google Scholar 

  4. Karellas A, and Vedantham S: Breast cancer imaging: a perspective for the next decade. Medical Physics 35:(11)4878–4897, 2008

    Article  Google Scholar 

  5. Glynn CG, Farria DM, Monsees BS, et al: Effect of transition to digital mammography on clinical outcomes. Radiology 260(3)664–670, 2011

    Article  Google Scholar 

  6. Gaur S, Dialani V, Slanetz PJ, et al: Architectural Distortion of the Breast. American Journal of Roentgenology 201:662–670, 2013

    Article  Google Scholar 

  7. Bahl M, Baker JA, Kinsey EN, et al: Architectural Distortion on Mammography: Correlation With Pathologic Outcomes and Predictors of Malignancy. American Journal of Roentgenology 205:(6)1339–1345, 2015

    Article  Google Scholar 

  8. D'Orsi CJ, Ed.: ACR BI-RADS Atlas, Breast Imaging Reporting and Data System, American College of Radiology, 2013

  9. Suleiman WI, Mcentee MF, Lewis SJ, et al: In the Digital Era, Architectural Distortion Remains a Challenging Radiological Task. Clinical Radiology 71:(1)e35–e40, 2016

    Article  CAS  Google Scholar 

  10. Jasionowska M, and Przelaskowski A: A two-step method for detection of architectural distortions in mammograms. Information Technology in Biomedicine 69:73–84, 2010

    Article  Google Scholar 

  11. Ray KM, Turner E, Sickles EA, et al: Suspicious Findings at Digital Breast Tomosynthesis Occult to Conventional Digital Mammography, Imaging Features and Pathology Findings. The Breast Journal, 2015, pp 1–5

  12. Dean JC and Ilvento CC: Improved Cancer Detection Using Computer-Aided Detection with Diagnostic and Screening Mammography - prospective study of 104 cancers. American Journal of Roentgenology 187:20–28, 2006

    Article  Google Scholar 

  13. Yang SK, Moon WK, Cho N, et al: Screening mammography-detected cancers: Sensitivity of a computer-aided detection system applied to full-field digital mammograms. Radiology 244:(1)104–111, 2007

    Article  Google Scholar 

  14. Kohli A and Jha S: Why CAD Failed in Mammography. Journal of the American College of Radiology 15:(3)535–537, 2018

    Article  Google Scholar 

  15. Rangayyan RM, Banik S, and Desautels JEL: Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer. Journal of Digital Imaging 23:(5)611–631, 2010

    Article  Google Scholar 

  16. Oliveira HCR, Mencattini A, Casti P, et al: Reduction of false-positives in a CAD scheme for automated detection of architectural distortion in digital mammography, in Medical Imaging 2018: Computer-Aided Diagnosis, Mori K and Petrick N, Eds. 10575. SPIE, 2018, pp 96

  17. Ojala T, Pietikäinen M, and Harwood D: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29:(1)51–59, 1996

    Article  Google Scholar 

  18. Guo Z, Zhang L, and Zhang D: A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing 19:(6)1657–1663, 2010

    Article  Google Scholar 

  19. Ferraz CT, Pereira Jr O, and Gonzaga A: Feature description based on center-symmetric local mapped patterns, in Proceedings of the 29th Annual ACM Symposium on Applied Computing (SAC’14). 2014, pp 39–44

  20. Ferraz CT, Pereira Jr O, Rosa MV, et al: Object recognition based on bag of features and a new local pattern descriptor. International Journal of Pattern Recognition and Artificial Intelligence 28:(8)14550101–145501032, 2014

    Article  Google Scholar 

  21. Negri T, Zhou F, Obradovic Z, et al: A robust descriptor for color texture classification under varying illumination, in International Conference on Computer Vision, Theory and Applications. 2017, pp 378–388

  22. Vieira RT, Negri TT, and Gonzaga A: Improving the classification of rotated images by adding the signal and magnitude information to a local texture descriptor. Multimedia Tools and Applications 77:(23)31041–31066, Dec 2018

    Article  Google Scholar 

  23. de Souza JM and Gonzaga A: Human iris feature extraction under pupil size variation using local texture descriptors. Multimidia Tools and Applications, 2019, pp. 1–28

  24. Oliveira HCR, Moraes DR, Reche GA, et al: A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images, in Proc. SPIE Medical Imaging 2017: Computer-Aided Diagnosis, Armato SG and Petrick NA Eds, 2017, 10134:101342U

    Google Scholar 

  25. Heikkilä M, Pietikäinen M, and Schmid C: Description of interest regions with local binary patterns. Pattern Recognition 42:425–436, 2009

    Article  Google Scholar 

  26. Lowe DG: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60:(2)91–110, 2004

    Article  Google Scholar 

  27. Liu X, Zhai L, Zhu T, et al: Multiple TBSVM-RFE for the detection of architectural distortion in mammographic images. Multimidia Tools and Applications 77:(12)15773–15802, 2018

    Article  Google Scholar 

  28. Kamra A, Jain VK, Singh S, et al: Characterization of architectural distortion in mammograms based on texture analysis using support vector machine classifier with clinical evaluation. Journal of Digital Imaging 29:(1)104–114, 2016

    Article  Google Scholar 

  29. Xue J, Zhang H, and Dana K: Deep texture manifold for ground terrain recognition, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 2018

  30. Oliveira HC, Melo CFE, Catani JH, et al: Exploratory learning with convolutional autoencoder for discrimination of architectural distortion in digital mammography, in Proc. of SPIE Medical Imaging 2019: Computer-Aided Diagnosis, Hahn HK and Mori K, Eds. SPIE, mar 2019, pp 8 

  31. Ionescu B, Benois-Pineau J, Piatrik T, et al: Fusion in Computer Vision: Understanding Complex Visual Content. Springer, Berlin, 2014

    Book  Google Scholar 

  32. Cheung Y and Deng J: Ultra local binary pattern for image texture analysis, in Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). 2014, pp 290–293

  33. Yuan J, Huang D, Zhu H, et al: Completed hybrid local binary pattern for texture classification, in 2014 International Joint Conference on Neural Networks (IJCNN). 2014, pp 2050–2057

    Article  Google Scholar 

  34. Pan Z, Fan H, and Zhang L: Texture classification using local pattern based on vector quantization. IEEE Transactions on Image Processing 24:(12)5379–5388, 2015

    Article  Google Scholar 

  35. Talab ARR and Shakoor MH: Fabric classification using new mapping of local binary pattern, in 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). 2018, pp 1–4

    Google Scholar 

  36. Deng W, Hu J, and Guo J: Compressive binary patterns: Designing a robust binary face descriptor with random-field eigenfilters, IEEE Transactions on Pattern Analysis and Machine Intelligence 41:(3)758–767, 2019

    Article  Google Scholar 

  37. Sonka M, Hlavac V, and Boyle R: Image Processing, Analysis, and Machine Vision. Springer US, 1993

    Book  Google Scholar 

  38. Suckling J, Parker J, Dance D, et al: The mammographic image analysis society digital mammogram database, in Exerpta Medica. International Congress Series 1069:375–378, 1994

    Google Scholar 

  39. Heath M, Bowyer K, Kopans D, et al: The digital database for screening mammography, in Proceedings of the 5th international workshop on digital mammography, 2000, pp 212–218

  40. Nemoto M, Honmura S, Shimizu A, et al: A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows. International Journal of Computer Assisted Radiology and Surgery 4:(1)27–36, 2009

    Article  Google Scholar 

  41. Minavathi, Murali S, and Dinesh MS: Model based approach for Detection of Architectural Distortions and Spiculated Masses in Mammograms. International Journal on Computer Science and Engineering 3:(11)3534–3546, 2011

  42. Rangayyan RM, Banik S, Chakraborty J, et al: Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms. International Journal of Computer Assisted Radiology and Surgery 8:(4)527–545, 2013

    Article  Google Scholar 

  43. Mohammadi E, Fatemizadeh E, Sheikhzadeh H, et al: A textural approach for recognizing architectural distortion in mammograms in 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP). 2013, pp 136–140

  44. Casti P, Mencattini A, Salmeri M, et al: Contour-independent detection and classification of mammographic lesions. Biomedical Signal Processing and Control 25:165–177, 2016

    Article  Google Scholar 

  45. Krizhevsky A, Sutskever I, and Hinton GE: Imagenet classification with deep convolutional neural networks, in Proceedings of the 25th International Conference on Neural Information Processing Systems Volume 1, ser. NIPS’12. Curran Associates Inc. USA, 2012, pp 1097–1105

  46. He K, Zhang X, Ren S, et al: Deep residual learning for image recognition, CoRR, vol. abs/1512.03385, 2015. [Online]. Available: http://arxiv.org/abs/1512.03385

  47. Rastegari M, Ordonez V, Redmon J, et al: ”Xnor-net: Imagenet classification using binary convolutional neural networks,” CoRR, vol. abs/1603.05279, 2016. [Online]. Available:http://arxiv.org/ abs/1603.05279

  48. Howard AG, Zhu M, Chen B, et al: ”Mobilenets: Efficient convolutional neural networks for mobile vision applications,” CoRR, vol. abs/1704.04861, 2017. [Online]. Available: http://arxiv.org/abs/1704.04861

  49. Deng H, Birdal T, and Ilic S, Ppfnet: Global context aware local features for robust 3d point matching, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 2018

  50. Bianco S, Cusano C, Napoletano P, et al: Improving cnn-based texture classification by color balancing. Journal of Imaging 3:33-07, 2017

    Article  Google Scholar 

  51. Na L, Xiankai L, Wan L, et al: Improving the separability of deep features with discriminative convolution filters for RSI classification. International Journal of Geo-Information 7:(3)95, 2018

  52. Yang Y, Feng C, Shen Y, et al: Foldingnet: Interpretable unsupervised learning on 3d point clouds, CoRR, vol. abs/1712.07262, 2017. [Online]. Available:http://arxiv.org/abs/1712.07262

  53. Zhao C: An autoencoder-based image descriptor for image matching and retrieval. PhD Dissertation, Wright State University, 1467, 2016. Available: https://corescholar.libraries.wright.edu/etd_all/1467

  54. Costa AC, Oliveira HCR, Borges LR, et al: Transfer learning in deep convolutional neural networks for detection of architectural distortion in digital mammography in 15th International Workshop on Breast Imaging (IWBI2020), H. Bosmans, N. Marshall, and C. V. Ongeval, Eds., vol. 11513, International Society for Optics and Photonics. SPIE, pp 170–177, 2020

  55. Akhtar Y and Mukherjee DP: Context-based ensemble classification for the detection of architectural distortion in a digitised mammogram. IET Image Processing 14:(4)603–614, 2020

    Article  Google Scholar 

  56. Schmidhuber, J: Deep learning in neural networks: An overview, Neural Networks, vol. 61, pp. 85–117, 2015. [Online]. Available:http://www.sciencedirect.com/science/article/pii/S0893608014002135

  57. Liu L, Fieguth P, Guo Y, et al: Local binary features for texture classification: Taxonomy and experimental study. Pattern Recognition 62:135–160, 2017

    Article  CAS  Google Scholar 

  58. Chen C, Zhang B, Su H, et al: Land-use scene classification using multi-scale completed local binary patterns. Signal, Image and Video Processing (SIViP) 10:(4)745–752, 2016

  59. Pereira Jr O, Ferraz CT, and Gonzaga A: Image correspondence using a fusion of local region descriptors, in XIV Workshop de Visão Computacional (WVC 2018), 2018.

  60. Ojala T, Pietikäinen M, and Mäenpää T: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24:(7)971–987, 2002

    Article  Google Scholar 

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and by the São Paulo Research Foundation (FAPESP), grant #2015/20812-5. The content is solely the responsibility of the authors and does not necessarily represent the official views of the CAPES or FAPESP. The authors would like to thank Rodrigo B. Vimieiro for his helpful advice and comments in this paper. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan XP GPU used for this research.

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Correspondence to Osmando Pereira Junior.

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Junior, O.P., Oliveira, H.C.R., Ferraz, C.T. et al. A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography. J Digit Imaging 34, 36–52 (2021). https://doi.org/10.1007/s10278-020-00391-5

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