Advertisement

Sādhanā

, 43:138 | Cite as

A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system

  • ESMA UZUNHISARCIKLI
  • VOLKAN GOREKE
Article
  • 62 Downloads

Abstract

Ultrasound imaging is an imaging technique for early detection of breast cancer. Breast Imaging Reporting and Data System (BI-RADS) lexicon, developed by The American College of Radiology, provides a standard for expert doctors to interpret the ultrasound images of breast cancer. This standard describes the features to classify the tumour as benign or malignant and it also categorizes the biopsy requirement as a percentage. Biopsy is an invasive method that doctors use for diagnosis of breast cancer. Computer-aided detection (CAD)/diagnosis systems that are designed to include the feature standards used in benign/malignant classification help the doctors in diagnosis but they do not provide enough information about the BI-RADS category of the mass. These systems classify the benign tumours with 90% biopsy possibility (BI-RADS-4) and with 2% biopsy possibility (BI-RADS-2) in the same category. There are some studies in the literature that make category classification via commonly used classifier methods but their success rates are low. In this study, a two-layer, high-success-rate classifier model based on Type-2 fuzzy inference is developed, which classifies the tumour as benign or malignant with its BI-RADS category by incorporating the opinions of the expert doctors. A 99.34% success rate in benign/malignant classification and a 92% success rate in category classification (BI-RADS 2, 3, 4, 5) were obtained in the accuracy tests. These results indicate that the CAD system is valuable as a means of providing a second diagnostic opinion when radiologists carry out mass diagnosis.

Keywords

Ultrasound CAD Type-2 fuzzy 

Notes

Acknowledgements

This study is supported by Erciyes University Scientific Research Projects unit with the ID of 6629. The authors are gratefull to Dr T S A Geertsma for supplying the ultrasound image database and Dr Bilge Oztoprak (Cumhuriyet University, Faculty of Medicine, Radiology Department) for commenting on ultrasound images medically.

References

  1. 1.
    Dhahbi S, Barhoumi W and Zagrouba E 2015 Breast cancer diagnosis in digitized mammograms using curvelet moments. Computer in Biology and Medicine 64: 79–90CrossRefGoogle Scholar
  2. 2.
    Dora L, Agraval S, Panda R and Abraham A 2017 Optimal breast cancer classification using Gauss–Newton representation based algorithm. Expert Systems with Applications 85: 134–145CrossRefGoogle Scholar
  3. 3.
    Lo C M, Lai Y C, Chou Y H and Chang R F 2015 Quantitative breast lesion classification based on multichannel distributions in shear-wave imaging. Computer Methods and Programs in Biomedicine 122: 354–361CrossRefGoogle Scholar
  4. 4.
    Reidt J M 1959 Diagnostic applications of ultrasound. Proceedings of the IRE 47: 1963–1967CrossRefGoogle Scholar
  5. 5.
    Shi X, Cheng H D, Hu L, Ju W and Tian J 2010 Detection and classification of masses in breast ultrasound images. Digital Signal Processing 20: 824–836.CrossRefGoogle Scholar
  6. 6.
    Stines J 2007 BI-RADS: Use in the French radiologic community. How to overcome with some difficulties. European Journal of Radiology 61: 224–234CrossRefGoogle Scholar
  7. 7.
    Hong A S, Rosen E L, Soo M S and Baker J 2005 BI-RADS for sonography: positive sonographic features. American Journal of Roentgenology 184: 1260–1265CrossRefGoogle Scholar
  8. 8.
    Park S, Shin D K and Kim J S 2013 Components of computer-aided diagnosis for breast ultrasound. IT Convergence Practice (INPRA) 1: 50–63Google Scholar
  9. 9.
    Liu B, Cheng H D, Huang J, Tian J, Tang X and Liu J 2010 Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images. Pattern Recognition 43: 280–298CrossRefzbMATHGoogle Scholar
  10. 10.
    Cheng H D, Shan J, Ju W, Guo Y and Zhang L 2010 Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognition 43: 299–317CrossRefzbMATHGoogle Scholar
  11. 11.
    Stoitsis J, Valavanis I, Mougiakakou S G, Golemati S, Nikita A and Nikita K S 2006 Computer aided diagnosis based on medical image processing and artificial intelligence methods. Nuclear Instruments and Methods in Physics Research 569: 591–595CrossRefGoogle Scholar
  12. 12.
    Sampaio W B, Diniz E M, Silva A C, Paiva A C and Gattass M 2011 Detection of masses in mammogram images using CNN, geostatistic functions and SVM. Computers in Biology and Medicine 41: 653–664CrossRefGoogle Scholar
  13. 13.
    Chiou H J, Chen C Y, Liu T C, Chiou S Y, Wang H K, Chou Y H and Chiang H K 2009 Computer-aided diagnosis of peripheral soft tissue masses based on ultrasound imaging. Computerized Medical Imaging and Graphics 33: 408–413CrossRefGoogle Scholar
  14. 14.
    Prabusankarlal K M, Manavalan R and Sivaranjani R 2017 An optimized non local means filter using automated clustering based preclassification through gap statistics for speckle reduction in breast ultrasound images. Applied Computing and Informatics 14(1): 48–54CrossRefGoogle Scholar
  15. 15.
    Deka B and Bora P K 2013 Removal of correlated speckle noise using sparse and overcomplete representations. Biomedical Signal Processing and Control 8: 520–533CrossRefGoogle Scholar
  16. 16.
    Latifoglu F 2013 A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: an ultrasound image application. Computer Methods and Programs in Biomedicine 111: 561–569CrossRefGoogle Scholar
  17. 17.
    Guo Y, Wang Y and Hou T 2011 Speckle filtering of ultrasonic images using a modified non local-based algorithm. Biomedical Signal Processing and Control 6: 129–138CrossRefGoogle Scholar
  18. 18.
    Shan J 2011 A fully automatic segmentation method for breast ultrasound images. Mphil Thesis, Utah State University, Logan, UtahGoogle Scholar
  19. 19.
    Sellami L, Sassi O B, Chtourou K and Hamida A B 2015 Breast cancer ultrasound images sequence exploration using BI-RADS features extraction: towards an advanced clinical aided tool for precise lesion characterization. IEEE Transactions on Nanobioscience 14: 740–745CrossRefGoogle Scholar
  20. 20.
    Huang Y L, Wang K L and Chen D R 2006 Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Computing and Applications 15: 164–169CrossRefGoogle Scholar
  21. 21.
    Zhou S, Shi J, Zhu J, Cai Y and Wang R 2013 Shearlet-based texture feature extraction for classification of breast tumor in ultrasound image. Biomedical Signal Processing and Control 8: 688–696CrossRefGoogle Scholar
  22. 22.
    Nasser M A, Melendez J, Moreno A, Omer O A and Puig D 2017 Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Engineering Applications of Artificial Intelligence 59: 84–92CrossRefGoogle Scholar
  23. 23.
    Xian G M 2010 An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Systems with Applications 37: 6737–6741CrossRefGoogle Scholar
  24. 24.
    Gonzales R and Woods R E 1992 Digital image processing, 2nd ed. Upper Saddle River, New Jersey 07458, USA: Prentice-HallGoogle Scholar
  25. 25.
    Ondimu S N and Murase H 2008 Effect of probability-distance based Markovian texture extraction on discrimination in biological imaging. Computers and Electronics in Agriculture 63: 2–12CrossRefGoogle Scholar
  26. 26.
    Haralick R M, Shanmugam K and Dinstein I 1973 Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6): 610–621CrossRefGoogle Scholar
  27. 27.
    Materka A and Strzelecki M 1998 Texture analysis methods – a review. Tecnical University of Lodz, Institute of Electronics, COST B11 Report, BrusselsGoogle Scholar
  28. 28.
    Amin K M, Shahin A I and Guo Y 2016 A novel breast tumor classification algorithm using neutrosophic score features. Measurement 81: 210–220CrossRefGoogle Scholar
  29. 29.
    Chang R F, Wu W J, Moon W K and Chen D R 2005 Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Research and Treatment 89: 179–185CrossRefGoogle Scholar
  30. 30.
    Menon R V, Raha P, Kothari S, Chakraborty, Chakrabarti I and Karim R 2015 Automated detection and classification of mass from breast ultrasound images. In: Proceedings of the NCVPRIPG Conference, 16–19 December, Patna, India, pp. 3–6 Google Scholar
  31. 31.
    Chen D R, Chien C L and Kuo Y F 2015 Computer-aided assessment of tumor grade for breast cancer in ultrasound images. Computational and Mathematical Methods in Medicine ID 914091, 6 pp.Google Scholar
  32. 32.
    Chen Y and Huang Q 2016 An approach based on biclustering and neural network for classification of lesions in breast ultrasound. In: Proceedings of the International Conference on Advanced Robotics and Mechatronics, 18–20 August 2016, Macau, China, pp. 597–601Google Scholar
  33. 33.
    Singh B K, Verma K and Thoke A S 2016 Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Systems with Applications 66: 114–123CrossRefGoogle Scholar
  34. 34.
    Wu W J, Lin S W and Moon W K 2012 Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images. Computerized Medical Imaging and Graphics 36: 627–633CrossRefGoogle Scholar
  35. 35.
    Liao R, Wan T and Qin Z 2011 Classification of benign and malignant breast tumors in ultrasound images based on multiple sonographic and textural features. In: Proceedings of the Third International Conference on Intelligent Human–Machine Systems and Cybernetics, 10 October 2011, Zhejiang, China, pp. 71–74Google Scholar
  36. 36.
    Singh B K, Verma K, Thoke A S and Suri J S 2017 Risk stratification of 2D ultrasound-based breast lesions using hybrid feature selection in machine learning paradigm. Measurement 105: 146–157CrossRefGoogle Scholar
  37. 37.
    Lee H and Chen Y P P 2015 Image based computer aided diagnosis system for cancer detection. Expert Systems with Applications 42: 5356–5365CrossRefGoogle Scholar
  38. 38.
    Prabusankarlal K M, Thirumoorthy P and Manavalan R 2015 Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound. Human-centric Computing and Information Sciences 5: 12CrossRefGoogle Scholar
  39. 39.
    Alvarenga A V, Infantosi A F C, Pereira W C A and Azevedo C M 2010 Assessing the performance of morphological parameters in distinguishing breast tumors on ultrasound images. Medical Engineering & Physics 32: 49–56CrossRefGoogle Scholar
  40. 40.
    Minavathi, Murali S and Dinesh M S 2012 Classification of mass in breast ultrasound images using image processing techniques. International Journal of Computer Applications 42: 29–36.CrossRefGoogle Scholar
  41. 41.
    Saranya P K and Samundeeswari E S 2016 A study on morphological and textural features for classifying breast lesion. International Journal of Innovative Research in Science, Engineering and Technology 5: 3267–3279Google Scholar
  42. 42.
    Nayeem M A R, Joadder A M, Shetu S A, Jamil F R and Helal A A 2014 Feature selection for breast cancer detection from ultrasound images. In: Proceedings of the International Conference on Informatics, Electronics & Vision (ICIEV), 23–24 May 2014, Dhaka, Bangladesh, pp. 1–6Google Scholar
  43. 43.
    Raza S, Goldkamp A L, Chikarmane S and Birdwell R L 2010 US of breast masses categorized as BI-RADS 3, 4, and 5: pictorial review of factors influencing clinical management. Radiographics 30: 1199–1213CrossRefGoogle Scholar
  44. 44.
    Huang Y L, Chen D R, Jiang Y R, Kuo S J, Wu H K and Moon W K 2008 Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound. Ultrasound in Obstetrics and Gynecology 32: 565–572CrossRefGoogle Scholar
  45. 45.
    George Y M, Zayed H H, Roushdy M I and Elbagoury B M 2014 Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Systems Journal 8: 949–964CrossRefGoogle Scholar
  46. 46.
    Gonzaga M A 2010 How accurate is ultrasound in evaluating palpable breast masses? Pan African Medical Journal 7: 1937–8688Google Scholar
  47. 47.
    Tan T, Platel B, Huisman B H, Sanchez C I, Mus R and Karssemeijer N. Computer-aided lesion diagnosis in automated 3-D breast ultrasound using coronal spiculation. IEEE Transactions on Medical Imaging 31: 1034–1042Google Scholar
  48. 48.
    Rangayyan R M, Mudigonda N R and Desautels J E L 2000 Boundary modelling and shape analysis methods for classification of mammographic masses. Medical & Biological Engineering & Computing 38: 487–496CrossRefGoogle Scholar
  49. 49.
    Shen L, Rangayyan R M and Desautels J E L 1994 Application of shape analysis to mammographic calcifications. IEEE Transactions on Medical Imaging 13: 263–274CrossRefGoogle Scholar
  50. 50.
    Joo S, Moon W K and Kim H C 2004 Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Transactions on Medical Imaging 23: 1292–1300CrossRefGoogle Scholar
  51. 51.
    Barbucha D, Nguyen N T and Batubara J 2015 New trends in intelligent information and database systems. In: Verma K, Singh B K, Tripathi P and Thoke A S (Eds). Review of feature selection algorithms for breast cancer ultrasound image. Switzerland: Springer International Publishing, 2015, pp. 23–32Google Scholar
  52. 52.
    Azar A T and El-Said S A 2013 Probabilistic neural network for breast cancer classification. Neural Computing and Applications 23: 1737–1751CrossRefGoogle Scholar
  53. 53.
    Moon W K, et al 2017 Computer-aided tumor diagnosis using shear wave breast elastography. Ultrasonics 78: 125–133CrossRefGoogle Scholar
  54. 54.
    Chen D R, Chang R F, Chen J C, Ho M F, Kuo S J, Chen S T, Hung S J and Moon W K 2005 Classification of breast ultrasound images using fractal feature. Journal of Clinical Imaging 29: 235–245CrossRefGoogle Scholar
  55. 55.
    Acharya U R, Ng W L, Rahmat K, Sudarshan V K, Koh J E W, Tan J H, Yeong C H and Ng K H 2017 Data mining framework for breast lesion classification in shear wave ultrasound: a hybrid feature paradigm. Biomedical Signal Processing and Control 33: 400–410CrossRefGoogle Scholar
  56. 56.
    Ding J, Cheng H D, Xian M, Zhang Y and Xu F 2015 Local-weighted citation—kNN algorithm for breast ultrasound image classification. Optik 126: 5188–5193CrossRefGoogle Scholar
  57. 57.
    Zadeh L A 1975 Fuzzy logic and approximate reasoning. Synthese 30: 407–428CrossRefzbMATHGoogle Scholar
  58. 58.
    Mamdani E H 1974 Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers 121: 1585–1588CrossRefGoogle Scholar
  59. 59.
    Choi B I, Chung F and Rhee H 2009 Interval type-2 fuzzy membership function generation methods for pattern recognition. Information Sciences 179: 2102–2122CrossRefzbMATHGoogle Scholar
  60. 60.
    Akgun A, Sezer E A, Nefeslioglu H A, Gokceoglu C and Pradhan B 2012 An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Computers and Geosciences 38: 23–34CrossRefGoogle Scholar
  61. 61.
    Mendel J M 2007 Type-2 fuzzy sets and systems: an overview. IEEE Computational Intelligence Magazine 2: 20–29Google Scholar
  62. 62.
    Castillo O and Melin P 2012 Type-2 fuzzy logic systems. In: Recent advances in interval type-2 fuzzy systems. Heidelberg–New York–Dordrecht–London: SpringerGoogle Scholar
  63. 63.
    Hosseini R, Qanadli S D, Barman S, Mazinani M, Ellis T and Dehmeshki J 2012 An automatic approach for learning and tuning Gaussian interval type-2 fuzzy membership functions applied to lung CAD classification system. IEEE Transactions on Fuzzy Systems 20: 224–234CrossRefGoogle Scholar
  64. 64.
    Juang C F, Huang R B and Lin Y Y 2009 A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing. IEEE Transactions on Fuzzy Systems 17: 1092–1105CrossRefGoogle Scholar
  65. 65.
    Zhai D and Mendel J M 2011 Uncertainty measures for general Type-2 fuzzy sets. Information Sciences 181: 503–518MathSciNetCrossRefzbMATHGoogle Scholar
  66. 66.
    Mendel J M, John R I and Liu F 2006 Interval type-2 fuzzy logic systems made simple. IEEE Transactions on Fuzzy Systems 14: 808–821CrossRefGoogle Scholar
  67. 67.
    Hidalgo D, Melin P and Castillo O 2012 An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms. Expert Systems with Applications 39: 4590–4598CrossRefGoogle Scholar
  68. 68.
    Raffei A F M, Asmuni H, Hassan R and Othman R M. A low lighting or contrast ratio visible iris recognition using iso-contrast limited adaptive histogram equalization. Knowledge-Based Systems 74: 40–48Google Scholar
  69. 69.
    Sha C, Hou J and Cui H 2016 A robust 2D Otsu’s thresholding method in image segmentation. Journal of Visual Communication and Image Representation. 41: 339–351CrossRefGoogle Scholar
  70. 70.
    Gomez W, Leija L, Pereira W C A and Infantosi A F C 2009 Morphological operators on the segmentation of breast ultrasound images. In: Proceedings of the Pan American Health Care Exchanges – PAHCE, 16–20 March 2009, Mexico City, Mexico, pp. 67–71Google Scholar
  71. 71.
    Moon W K, Lo M C, Cho N, Chang J M, Huang C S, Chen J H and Chang R F 2013 Computer-aided diagnosis of breast masses using quantified BI-RADS findings. Computer Methods and Programs in Biomedicine 111: 84–92CrossRefGoogle Scholar
  72. 72.
    Giger M L 2000 Computer-aided diagnosis of breast lesions in medical images. Computing in Medicine 58: 39–45Google Scholar
  73. 73.
    Lo C M, Chang Y C, Yang Y W, Huang C S and Chang R F 2015 Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography. Computers in Biology and Medicine 64: 91–100CrossRefGoogle Scholar
  74. 74.
    Moon W K, Lo C M, Chang J M, Huang C S, Chen J H, and Chang R F 2013 Quantitative Ultrasound Analysis for Classification of BI-RADS Category 3 Breast Masses. Journal of Digital Imaging 26: 1091–1098CrossRefGoogle Scholar
  75. 75.
    Miranda G H B and Felipe J C 2015 Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Computers in Biology and Medicine 64: 334–346CrossRefGoogle Scholar
  76. 76.
    You H, Ma Z, Tang Y, Wang Y, Yan J, Cen K, Ni M and Huang Q 2017 Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. Waste Management 68: 186–197CrossRefGoogle Scholar
  77. 77.
    Mousa R, Munib Q and Moussa A 2005 Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert Systems with Applications 28: 713–723CrossRefGoogle Scholar
  78. 78.
    Iraji M S 2017 Multi-layer architecture for adaptive fuzzy inference system with a large number of input features. Cognitive Systems Research 42: 23–41CrossRefGoogle Scholar
  79. 79.
    Mendel J M 2013 On KM algorithms for solving type-2 fuzzy set problems. IEEE Transactions on Fuzzy Systems 21: 426–446CrossRefGoogle Scholar
  80. 80.
    Wu D and Mendel J M 2014 Designing practical interval type-2 fuzzy logic systems made simple. In: Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 6–11 July 2014, Beijing, China, pp. 800–807Google Scholar
  81. 81.
    Chumklin S, Auephanwiriyakul S and Umpon N T 2010 Microcalcification detection in mammograms using interval type-2 fuzzy logic system with automatic membership function generation. In: Proceedings of Fuzzy Systems (FUZZ), 2010 IEEE International Conference, 18–23 July 2010, Barcelona, SpainGoogle Scholar
  82. 82.
    Treesatayapun C and Uatrongjit S 2005 Adaptive controller with fuzzy rules emulated structure and its applications. Engineering Applications of Artificial Intelligence 18: 603–615CrossRefGoogle Scholar
  83. 83.
    Nozohour-Leilabady B N and Fazelabdolabadi B 2016 On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology. Petroleum 2: 79–89CrossRefGoogle Scholar
  84. 84.
    Lin C, Hou Y, Chen T and Chen K 2014 Breast nodules computer-aided diagnostic system design using fuzzy cerebellar model neural networks. IEEE Transactions on Fuzzy Systems 22: 693–699CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  1. 1.Kayseri Vocational CollegeErciyes UniversityKayseriTurkey
  2. 2.Kangal Vocational CollegeCumhuriyet UniversitySivasTurkey

Personalised recommendations