Skip to main content

Advertisement

SpringerLink
Log in

Development of breast papillary index for differentiation of benign and malignant lesions using ultrasound images

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Papillary breast lesions include a wide spectrum of pathologies ranging from benign to malignant. The word papillary originates from finger-like projections, or papules, which are seen when these lesions are projected under a microscope. Papillary breast lesions have an array of radiological features at presentations; hence differentiation between benign and malignant based on imaging features is challenging. Histopathological diagnosis is crucial for the distinction and further management of the lesions. Traditionally, tumor and ductal excision is the treatment of choice for malignant and atypical or benign papilloma with imaging discordance. However, current clinical practice guidance advocates complete surgical excision, even for asymptomatic and purely benign papillomas diagnosed on core needle biopsy, as they are highly associated with atypia and malignant upstage on subsequent surgery. Computer aided diagnosis (CAD) is a non-invasive method of diagnosing medical signals/images using advanced image processing followed by soft computing techniques. In this study, we have developed a non-invasive CAD system for differentiating benign versus malignant papillary breast lesions using bi-dimensional empirical mode decomposition (BEMD) and the discrete cosine transform (DCT) followed by locality sensitive discriminant analysis (LSDA). The developed model is validated using a large collection of ultrasound images of papillary breast lesions, and achieved a maximum performance of 98.63% accuracy. We have also developed a breast papillary index, which may in the future be used as a substitute for the conventional soft computing techniques. The developed model can be utilized as a tool to assist radiologists in their routine clinical practice after validation with a larger database.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Acharya UR, Faust O, Sree SV, Molinari F, Suri JS (2012a) ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform. Comput Methods Programs Biomed 107(2):233–241

    Article  Google Scholar 

  • Acharya UR, Ng EY-K, Tan J-H, Sree SV, Ng K-H (2012b) An integrated index for the identification of diabetic retinopathy stages using texture parameters. J Med Syst 36(3):2011–2020

    Article  Google Scholar 

  • Acharya UR, Faust O, Sree SV, Ghista DN, Dua S, Joseph P, Ahamed VT, Janarthanan N, Tamura T (2013) An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes. Comput Methods Biomech Biomed Eng 16(2):222–234

    Article  Google Scholar 

  • Acharya UR, Fujita H, Sudarshan VK, Mookiah MRK, Koh JE, Tan JH, Hagiwara Y, Chua CK, Junnarkar SP, Vijayananthan A, Ng KH (2015a) An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images. Inf Fusion 31:43–53

    Article  Google Scholar 

  • Acharya UR, Fujita H, Sudarshan VK, Sree VS, Eugene LWJ, Ghista DN, San Tan R (2015b) An integrated index for detection of sudden cardiac death using discrete wavelet transform and nonlinear features. Knowl-Based Syst 83:149–158

    Article  Google Scholar 

  • Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JEW, Hong TJ, Sudarshan VK, Vijayananthan A, Yeong CH, Gudigar A, Ng KH (2016) Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images. Comput Biol Med 79:250–258

    Article  Google Scholar 

  • Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Krishna Rao A, Hagiwara Y, Chua CK, Laude A (2017a) Automated diabetic macular edema (DME) grading system using DWT DCT features and maculopathy index. Comput Biol Med 84:59–68

    Article  Google Scholar 

  • Acharya UR, Sudarshan VK, Koh JEW, Martis RJ, Tan JH, Shu Lih Oh, Muhammad A, Hagiwara Y, Mookiah MRK, Chua KP, Chua CK, Tan RS (2017b) Application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals. Biomed Signal Process Control 31:31–43

    Article  Google Scholar 

  • Agoumi M, Giambattista J, Hayes MM (2016) Practical considerations in breast papillary lesions: a review of the literature. Arch Pathol Lab Med 140(8):770–790

    Article  Google Scholar 

  • Bernik SF, Troob S, Ying BL, Simpson SA, Axelrod DM, Siegel B et al (2009) Papillary lesions of the breast diagnosed by core needle biopsy: 71 cases with surgical follow-up. Am J Surg 197(4):473–478

    Article  Google Scholar 

  • Bianchi S, Bendinelli B, Saladino V, Vezzosi V, Brancato B, Nori J et al (2015) Non-malignant breast papillary lesions-b3 diagnosed on ultrasound-guided 14-gauge needle core biopsy: analysis of 114 cases from a single institution and review of the literature. Pathol Oncol Res 21(3):535–546

    Article  Google Scholar 

  • Cai D, He X, Zhou K, Han J, Bao H (2007) Locality sensitive discriminant analysis, Proceedings of the 20th International Joint Conference on Artificial Intelligence IJCAI’07, pp. 708–713.

  • Chang JM, Moon WK, Cho N, Han W, Noh D-Y, Park I-A et al (2011) Management of ultrasonographically detected benign papillomas of the breast at core needle biopsy. Am J Roentgenol 196(3):723–729

    Article  Google Scholar 

  • Duda R, Hart P, Stork D (2012) Pattern classification. Wiley, New York

    MATH  Google Scholar 

  • Eiada R, Chong J, Kulkarni S, Goldberg F, Muradali D (2012) Papillary lesions of the breast: MRI, ultrasound, and mammographic appearances. Am J Roentgenol 198(2):264–271

    Article  Google Scholar 

  • Ghista DN (2009) Nondimensional physiological indices for medical assessment. J Mech Med Biol 9:643–669

    Article  Google Scholar 

  • Gonzalez RC, Woods RE (2005) Book on “digital image processing”, 4th edn. Pearson, London

    Google Scholar 

  • Jain AK (1989) Fundamentals of digital image processing. Prentice Hall, Englewood Cliffs, pp 150–153

    MATH  Google Scholar 

  • Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160. https://doi.org/10.1109/TASSP.1981.1163711

    Article  MathSciNet  MATH  Google Scholar 

  • Kuzmiak CM, Lewis MQ, Zeng D, Liu X (2014) Role of sonography in the differentiation of benign, high-risk, and malignant papillary lesions of the breast. J Ultrasound Med 33(9):1545–1552

    Article  Google Scholar 

  • Lewis JT, Hartmann LC, Vierkant RA, Maloney SD, Pankratz VS, Allers TM et al (2006) An analysis of breast cancer risk in women with single, multiple, and atypical papilloma. Am J Surg Pathol 30(6):665–672

    Article  Google Scholar 

  • Mookiah MRK, Venkatraghavan V, Acharya UR, Pal M, Paul RR, Min LC, Ray AK, Chatterjee J, Chakraborty C (2012) Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm. Micron 43(2):352–364

    Google Scholar 

  • Muttarak M, Lerttumnongtum P, Chaiwun B, Peh WC (2008) Spectrum of papillary lesions of the breast: clinical, imaging, and pathologic correlation. Am J Roentgenol 191(3):700–707

    Article  Google Scholar 

  • Nunes JC, Bouaoune Y, Delechelle E, Niang O, Bunel P (2003) Image analysis by bidimensional empirical mode decomposition. Image Vis Comput 21(12):1019–1026. https://doi.org/10.1016/S0262-8856(03)00094-5

    Article  MATH  Google Scholar 

  • Nunes JC, Guyot S, Deléchelle E (2005) Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Mach Vision Appl 16(3):177–188

    Article  Google Scholar 

  • Pennebaker WB, Joan LM (1993) JPEG: still image data compression standard. Van Nostrand Reinhold, New York

    Google Scholar 

  • Raghavendra U, Rajendra Achary U, Fujita H, Gudigar A, Tan JH, Chokkadi S (2016a) Application of Gabor wavelet and locality sensitive discriminant analysis for automated identification of breast cancer using digitized mammogram images. Appl Soft Comput 46:151–161

    Article  Google Scholar 

  • Raghavendra U, Rajendra Acharya U, Ng EYK, Tan J-H, Gudigar A (2016b) An integrated index for breast cancer identification using histogram of oriented gradient and kernel locality preserving projection features extracted from thermograms. Quant Infrared Thermogr 13(2):195–209

    Article  Google Scholar 

  • Raghavendra U, Fujita H, Gudigar A, Shetty R, Nayak K, Pai U, Jyothi Samanth U, Acharya R (2017) Automated technique for coronary artery disease characterization and classification using DD-DTDWT in ultrasound images. Biomed Signal Process Control 40:324–334

    Article  Google Scholar 

  • Raghavendra U, Acharya UR, Gudigar A, Tan JH, Fujita H, Hagiwara Y, Molinari F, Kongmebhol P, Ng KH (2017b) Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions, Ultrasonics, pp. 202–210.

  • Rosen EL, Bentley RC, Baker JA, Soo MS (2002) Imaging-guided core needle biopsy of papillary lesions of the breast. Am J Roentgenol 179(5):1185–1192

    Article  Google Scholar 

  • Sarica O, Dokdok M (2018) Imaging findings in papillary breast lesions: an analysis of ductal findings on magnetic resonance imaging and ultrasound. J Comput Assist Tomogr 42(4):542–551

    Article  Google Scholar 

  • Skandarajah AR, Field L, Mou AYL, Buchanan M, Evans J, Hart S et al (2008) Benign papilloma on core biopsy requires surgical excision. Ann Surg Oncol 15(8):2272

    Article  Google Scholar 

  • Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118

    Article  Google Scholar 

  • Swapp RE, Glazebrook KN, Jones KN, Brandts HM, Reynolds C, Visscher DW et al (2013) Management of benign intraductal solitary papilloma diagnosed on core needle biopsy. Ann Surg Oncol 20(6):1900–1905

    Article  Google Scholar 

  • T-test, Student’s t-tests, Information https://www.physics.csbsju.edu/stats/t-test.html. Accessed 03 Apr 2020

  • Ueng S-H, Mezzetti T, Tavassoli FA (2009) Papillary neoplasms of the breast: a review. Arch Pathol Lab Med 133(6):893–907

    Article  Google Scholar 

  • Valdes EK, Tartter PI, Genelus-Dominique E, Guilbaud D-A, Rosenbaum-Smith S, Estabrook A (2006) Significance of papillary lesions at percutaneous breast biopsy. Ann Surg Oncol 13(4):480–482

    Article  Google Scholar 

  • Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 1. Wiley, New York

    MATH  Google Scholar 

  • Wang X, Wong BS, Guan TC (2004) Image enhancement for radiography inspection, Proc. SPIE 5852, Third International Conference on experimental mechanics and Third Conference of the Asian committee on experimental mechanics, p. 462.

  • Wang L-J, Wu P, Li X-X, Luo R, Wang D-B, Guan W-B (2018) Magnetic resonance imaging features for differentiating breast papilloma with high-risk or malignant lesions from benign papilloma: a retrospective study on 158 patients. World J Surg Oncol 16(1):234

    Article  Google Scholar 

  • Wen X, Cheng W (2013) Nonmalignant breast papillary lesions at core-needle biopsy: a meta-analysis of underestimation and influencing factors. Ann Surg Oncol 20(1):94–101

    Article  Google Scholar 

  • Wiratkapun C, Keeratitragoon T, Lertsithichai P, Chanplakorn N (2013) Upgrading rate of papillary breast lesions diagnosed by core-needle biopsy. Diagn Interv Radiol 19(5):371

    Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the essential contributions of Dr. Syarifah Muna- Izzati Sayed Abul Khair.

Funding

This work was supported in part by the University of Malaya Fundamental Research Grant Scheme (FP017-2019A).

Author information

Authors and Affiliations

  1. School of Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore

    The-Hanh Pham, Joel En Wei Koh & U. Rajendra Acharya

  2. Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India

    U. Raghavendra & Anjan Gudigar

  3. Department of Biomedical Imaging, University Malaya Research Imaging Centre, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Wai Yee Chan, Marlina Tanty Ramli Hamid, Kartini Rahmat, Farhana Fadzli & Kwan Hoong Ng

  4. Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia

    Marlina Tanty Ramli Hamid

  5. School of Science and Technology, Singapore University of Social Sciences, Clementi, 599494, Singapore

    Chui Ping Ooi

  6. Department of Medicine, Columbia University Medical Center, New York, NY, 10032, USA

    Edward J. Ciaccio

  7. Department of Computer Science, Iwate Prefectural University, Takizawa, 020-0693, Japan

    Hamido Fujita

  8. Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan

    U. Rajendra Acharya

  9. International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan

    U. Rajendra Acharya

Authors
  1. The-Hanh Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. U. Raghavendra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joel En Wei Koh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anjan Gudigar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wai Yee Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marlina Tanty Ramli Hamid
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kartini Rahmat
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Farhana Fadzli
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kwan Hoong Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Chui Ping Ooi
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Edward J. Ciaccio
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hamido Fujita
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. U. Rajendra Acharya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to U. Rajendra Acharya.

Ethics declarations

Conflict of interest

There is no conflict of interest in this work.

Code availability

Custom code.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pham, TH., Raghavendra, U., Koh, J.E.W. et al. Development of breast papillary index for differentiation of benign and malignant lesions using ultrasound images. J Ambient Intell Human Comput 12, 2121–2129 (2021). https://doi.org/10.1007/s12652-020-02310-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02310-6

Keywords

Search

Navigation