• Jwan N. Saeed IT Department, Duhok Technical Institute, Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq




Breast cancer, breast ultrasonography images (BUS), computer aided diagnoses (CAD), early detection, segmentation.


The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges associated with their nature. This paper presents a survey on several techniques of ultrasonography images segmentation including threshold based, region based, watershed, active contour and learning based techniques, their merits, and demerits. This can provide significant insights for CAD developers or researchers to advance this field.


Download data is not yet available.


S. Punitha, A. Amuthan, and K. S. Joseph, "Benign and malignant breast cancer segmentation using optimized region growing technique," Future Computing and Informatics Journal, vol. 3, pp. 348-358, 2018.
2. T. Tan, B. Platel, R. M. Mann, H. Huisman, and N. Karssemeijer, "Chest wall segmentation in automated 3D breast ultrasound scans," Medical image analysis, vol. 17, pp. 1273-1281, 2013.
3. A. Jalalian, S. B. Mashohor, H. R. Mahmud, M. I. B. Saripan, A. R. B. Ramli, and B. Karasfi, "Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review," Clinical imaging, vol. 37, pp. 420-426, 2013.
4. K. Drukker, M. L. Giger, C. J. Vyborny, and E. B. Mendelson, "Computerized detection and classification of cancer on breast ultrasound1," Academic radiology, vol. 11, pp. 526-535, 2004.
5. T. Ungi, G. Gauvin, A. Lasso, C. T. Yeo, P. Pezeshki, T. Vaughan, et al., "Navigated breast tumor excision using electromagnetically tracked ultrasound and surgical instruments," IEEE Transactions on Biomedical Engineering, vol. 63, pp. 600-606, 2015.
6. [6] R.-F. Chang, W.-J. Wu, W. K. Moon, Y.-H. Chou, and D.-R. Chen, "Support vector machines for diagnosis of breast tumors on US images," Academic radiology, vol. 10, pp. 189-197, 2003.
7. P. D. Velusamy and P. Karandharaj, "Medical image processing schemes for cancer detection: A survey," in 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), 2014, pp. 1-6.
8. C. Varma and O. Sawant, "An Alternative Approach to Detect Breast Cancer Using Digital Image Processing Techniques," in 2018 International Conference on Communication and Signal Processing (ICCSP), 2018, pp. 0134-0137.
9. C.-Y. Liu, C.-Y. Hsu, Y.-H. Chou, and C.-M. Chen, "A multi-scale tumor detection algorithm in whole breast sonography incorporating breast anatomy and tissue morphological information," in 2014 IEEE Healthcare Innovation Conference (HIC), 2014, pp. 193-196.
10. R.-F. Chang, W.-J. Wu, W. K. Moon, and D.-R. Chen, "Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors," Breast cancer research and treatment, vol. 89, p. 179, 2005.
11. L. Zhang, Y. Ren, C. Huang, and F. Liu, "A novel automatic tumor detection for breast cancer ultrasound Images," in 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011, pp. 401-404.
12. K. S. Camilus and V. Govindan, "A Review on Graph Based Segmentation," International Journal of Image, Graphics & Signal Processing, vol. 4, 2012.
13. C.-C. Kang, W.-J. Wang, and C.-H. Kang, "Image segmentation with complicated background by using seeded region growing," AEU-International Journal of Electronics and Communications, vol. 66, pp. 767-771, 2012.
14. K. Prabusankarlal, P. Thirumoorthy, and R. Manavalan, "Computer aided breast cancer diagnosis techniques in ultrasound: a survey," Journal of Medical Imaging and Health Informatics, vol. 4, pp. 331-349, 2014.
15. S. Mazaheri, P. S. B. Sulaiman, R. Wirza, F. Khalid, S. Kadiman, M. Z. Dimon, et al., "Echocardiography image segmentation: A survey," in 2013 International Conference on Advanced Computer Science Applications and Technologies, 2013, pp. 327-332.
16. Q. Huang, Y. Luo, and Q. Zhang, "Breast ultrasound image segmentation: a survey," International journal of computer assisted radiology and surgery, vol. 12, pp. 493-507, 2017.
17. Y.-L. Huang, K.-L. Wang, and D.-R. Chen, "Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines," Neural Computing & Applications, vol. 15, pp. 164-169, 2006.
18. M. Xian, Y. Zhang, H.-D. Cheng, F. Xu, K. Huang, B. Zhang, et al., A benchmark for breast ultrasound image segmentation (BUSIS): Infinite Study, 2018.
19. M. Lal, L. Kaur, and S. Gupta, "Modified spatial neutrosophic clustering technique for boundary extraction of tumours in B-mode BUS images," IET Image Processing, vol. 12, pp. 1338-1344, 2018.
20. R. Guo, G. Lu, B. Qin, and B. Fei, "Ultrasound imaging technologies for breast cancer detection and management: a review," Ultrasound in medicine & biology, vol. 44, pp. 37-70, 2018.
21. W. Gomez, A. Rodriguez, W. Pereira, and A. Infantosi, "Feature selection and classifier performance in computer-aided diagnosis for breast ultrasound," in 2013 10th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT), 2013, pp. 1-5.
22. A. Ibrahim, S. Mohammed, and H. A. Ali, "Breast Cancer Detection and Classification Using Thermography: A Review," in International Conference on Advanced Machine Learning Technologies and Applications, 2018, pp. 496-505.
23. M. M. Eapena, M. S. J. A. Ancelita, and G. Geetha, "Segmentation of tumors from ultrasound images with PAORGB," Procedia Computer Science, vol. 50, pp. 663-668, 2015.
24. J. Zhao, F. Shao, Y. Xu, X. Zhang, and W. Huang, "An improved Chan-Vese model without reinitialization for medical image segmentation," in 2010 3rd International Congress on Image and Signal Processing, 2010, pp. 1317-1321.
25. Y. B. Fadhel, S. Ktata, and T. Kraiem, "Cardiac scintigraphic images segmentation techniques," in 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2016, pp. 364-369.
26. M. R. Khokher, A. Ghafoor, and A. M. Siddiqui, "Image segmentation using multilevel graph cuts and graph development using fuzzy rule-based system," IET image processing, vol. 7, pp. 201-211, 2013.
27. K. Saini, M. Dewal, and M. Rohit, "Ultrasound imaging and image segmentation in the area of ultrasound: a review," International Journal of advanced science and technology, vol. 24, 2010.
28. A. Bali and S. N. Singh, "A review on the strategies and techniques of image segmentation," in 2015 Fifth International Conference on Advanced Computing & Communication Technologies, 2015, pp. 113-120.
29. D. Kaur and Y. Kaur, "Various image segmentation techniques: a review," International Journal of Computer Science and Mobile Computing, vol. 3, pp. 809-814, 2014.
30. P. S. Rodrigues and G. A. Giraldi, "Improving the non-extensive medical image segmentation based on Tsallis entropy," Pattern Analysis and Applications, vol. 14, pp. 369-379, 2011.
31. T. Nayak, N. Bhat, V. Bhat, S. Shetty, M. Javed, and P. Nagabhushan, "Automatic Segmentation and Breast Density Estimation for Cancer Detection Using an Efficient Watershed Algorithm," in Data Analytics and Learning, ed: Springer, 2019, pp. 347-358.
32. M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," Journal of Electronic imaging, vol. 13, pp. 146-166, 2004.
33. M. Sahar, H. A. Nugroho, I. Ardiyanto, and L. Choridah, "Automated detection of breast cancer lesions using adaptive thresholding and morphological operation," in 2016 International Conference on Information Technology Systems and Innovation (ICITSI), 2016, pp. 1-4.
34. L. Liu, K. Li, W. Qin, T. Wen, L. Li, J. Wu, et al., "Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images," Medical & biological engineering & computing, vol. 56, pp. 183-199, 2018.
35. E. Samundeeswari, P. Saranya, and R. Manavalan, "Segmentation of breast ultrasound image using regularized K-means (ReKM) clustering," in 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2016, pp. 1379-1383.
36. W. Khan, "Image segmentation techniques: A survey," Journal of Image and Graphics, vol. 1, pp. 166-170, 2013.
37. P. Agrawal, A. Kajla, and M. N. Raverkar, "Image Segmentation Techniques–A Review."
38. X. Li, C. Yang, and S. Wu, "Automatic segmentation algorithm of breast ultrasound image based on improved level set algorithm," in 2016 IEEE International Conference on Signal and Image Processing (ICSIP), 2016, pp. 319-322.
39. M. Lal and L. Kaur, "Automatic seed point selection in B-Mode breast ultrasound images," in Sensors and Image Processing, ed: Springer, 2018, pp. 131-138.
40. X. Jiang, Y. Guo, H. Chen, Y. Zhang, and Y. Lu, "An Adaptive Region Growing Based on Neutrosophic Set in Ultrasound Domain for Image Segmentation," IEEE Access, vol. 7, pp. 60584-60593, 2019.
41. S. Beucher and C. Lantuejoul, "Use of watersheds in contour detection. Int," in Workshop on Image Processing, CCETT/IRISA, Rennes, France, 1979.
42. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, "Segmentation and classification of hyperspectral images using watershed transformation," Pattern Recognition, vol. 43, pp. 2367-2379, 2010.
43. R. A. Husain, A. S. Zayed, W. M. Ahmed, and H. S. Elhaji, "Image segmentation with improved watershed algorithm using radial bases function neural networks," in 2015 16th International conference on sciences and techniques of automatic control and computer engineering (STA), 2015, pp. 121-126.
44. H. Ng, S. Ong, K. Foong, P. Goh, and W. Nowinski, "Medical image segmentation using k-means clustering and improved watershed algorithm," in 2006 IEEE Southwest Symposium on Image Analysis and Interpretation, 2006, pp. 61-65.
45. W. Gomez, L. Leija, W. Pereira, and A. Infantosi, "Morphological operators on the segmentation of breast ultrasound images," in 2009 Pan American Health Care Exchanges, 2009, pp. 67-71.
46. H. A. Nugroho, Y. Triyani, M. Rahmawaty, and I. Ardiyanto, "Breast ultrasound image segmentation based on neutrosophic set and watershed method for classifying margin characteristics," in 2017 7th IEEE International Conference on System Engineering and Technology (ICSET), 2017, pp. 43-47.
47. H. A. Nugroho, Y. Triyani, M. Rahmawaty, and I. Ardiyanto, "Analysis of margin sharpness for breast nodule classification on ultrasound images," in 2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE), 2017, pp. 1-5.
48. Y. Bafna, K. Verma, L. Panigrahi, and S. P. Sahu, "Automated boundary detection of breast cancer in ultrasound images using watershed algorithm," in Ambient communications and computer systems, ed: Springer, 2018, pp. 729-738.
49. L. Fang, T. Qiu, H. Zhao, and F. Lv, "A hybrid active contour model based on global and local information for medical image segmentation," Multidimensional Systems and Signal Processing, vol. 30, pp. 689-703, 2019.
50. [50] K. Kirimasthong, A. Rodtook, W. Lohitvisate, and S. S. Makhanov, "Automatic initialization of active contours in ultrasound images of breast cancer," Pattern Analysis and Applications, pp. 1-10, 2018.
51. R. V. Menon, P. Raha, S. Kothari, S. Chakraborty, I. Chakrabarti, and R. Karim, "Automated detection and classification of mass from breast ultrasound images," in 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2015, pp. 1-4.
52. S. Lankton and A. Tannenbaum, "Localizing region-based active contours," IEEE transactions on image processing, vol. 17, pp. 2029-2039, 2008.
53. M. Dinesh, "Classification of Mass in Breast Ultrasound Images using Image Processing Techniques," International Journal of Computer Applications, vol. 975, p. 8887.
54. L. Panigrahi, K. Verma, and B. K. Singh, "An enhancement in automatic seed selection in breast cancer ultrasound images using texture features," in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016, pp. 1096-1102.
55. S. Selvan and S. S. Devi, "Automatic seed point selection in ultrasound echography images of breast using texture features," Biocybernetics and Biomedical Engineering, vol. 35, pp. 157-168, 2015.
56. T. Prabhakar and S. Poonguzhali, "Automatic detection and classification of benign and malignant lesions in breast ultrasound images using texture morphological and fractal features," in 2017 10th Biomedical Engineering International Conference (BMEiCON), 2017, pp. 1-5.
57. S. Eser and A. Derya, "A new edge detection approach via neutrosophy based on maximum norm entropy," Expert Systems with Applications, vol. 115, pp. 499-511, 2019.
58. M. Lotfollahi, M. Gity, J. Y. Ye, and A. M. Far, "Segmentation of breast ultrasound images based on active contours using neutrosophic theory," Journal of Medical Ultrasonics, vol. 45, pp. 205-212, 2018.
59. M. Xian, Y. Zhang, H.-D. Cheng, F. Xu, B. Zhang, and J. Ding, "Automatic breast ultrasound image segmentation: A survey," Pattern Recognition, vol. 79, pp. 340-355, 2018.
60. H.-D. Cheng, J. Shan, W. Ju, Y. Guo, and L. Zhang, "Automated breast cancer detection and classification using ultrasound images: A survey," Pattern recognition, vol. 43, pp. 299-317, 2010.
61. N. Arunkumar, M. A. Mohammed, S. A. Mostafa, D. A. Ibrahim, J. J. Rodrigues, and V. H. C. de Albuquerque, "Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks," Concurrency and Computation: Practice and Experience, p. e4962, 2018.
62. N. Arunkumar, M. A. Mohammed, M. K. A. Ghani, D. A. Ibrahim, E. Abdulhay, G. Ramirez-Gonzalez, et al., "K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor," Soft Computing, pp. 1-14, 2018.
63. D. Q. Zeebaree, H. Haron, and A. M. Abdulazeez, "Gene Selection and Classification of Microarray Data Using Convolutional Neural Network," in 2018 International Conference on Advanced Science and Engineering (ICOASE), 2018, pp. 145-150.
64. N. Wang, C. Bian, Y. Wang, M. Xu, C. Qin, X. Yang, et al., "Densely deep supervised networks with threshold loss for cancer detection in automated breast ultrasound," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018, pp. 641-648.
65. X. Xie, F. Shi, J. Niu, and X. Tang, "Breast ultrasound image classification and segmentation using convolutional neural networks," in Pacific Rim Conference on Multimedia, 2018, pp. 200-211.
66. R. Almajalid, J. Shan, Y. Du, and M. Zhang, "Development of a Deep-Learning-Based Method for Breast Ultrasound Image Segmentation," in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018, pp. 1103-1108.
67. D. Q. Zeebaree, H. Haron, A. M. Abdulazeez, and D. A. Zebari, "Machine learning and Region Growing for Breast Cancer Segmentation," in 2019 International Conference on Advanced Science and Engineering (ICOASE), 2019, pp. 88-93.
68. D. Q. Zeebaree, H. Haron, A. M. Abdulazeez, and D. A. Zebari, "Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer," in 2019 International Conference on Advanced Science and Engineering (ICOASE), 2019, pp. 106-111.
69. M. B. Abdulrazzaq and J. N. Saeed, "A Comparison of Three Classification Algorithms for Handwritten Digit Recognition," in 2019 International Conference on Advanced Science and Engineering (ICOASE), 2019, pp. 58-63.
70. P. Raha, R. V. Menon, and I. Chakrabarti, "Fully automated computer aided diagnosis system for classification of breast mass from ultrasound images," in 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2017, pp. 48-51.
71. Zebari, D. A., Haron, H., Zeebaree, S. R., & Zeebaree, D. Q. (2019, April). Enhance the Mammogram Images for Both Segmentation and Feature Extraction Using Wavelet Transform. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 100-105). IEEE.
72. Mahmood, M. R., Abdulazeez, A. M., & Orman, Z. (2018, October). Dynamic Hand Gesture Recognition System for Kurdish Sign Language Using Two Lines of Features. In 2018 International Conference on Advanced Science and Engineering (ICOASE) (pp. 42-47). IEEE.



How to Cite

Saeed, J. N. (2020). A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES. Academic Journal of Nawroz University, 9(1), 1–14. https://doi.org/10.25007/ajnu.v9n1a523