Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3813–3832 | Cite as

Wavelet energy entropy and linear regression classifier for detecting abnormal breasts

  • Yi Chen
  • Yin Zhang
  • Hui-Min Lu
  • Xian-Qing Chen
  • Jian-Wu Li
  • Shui-Hua Wang
Article

Abstract

Breast abnormalities are the early symptoms of breast cancers. They may also bring in psychoemotional stresses to women. In this study, we developed a new automatic program based on wavelet energy entropy (WEE) and linear regression classifier (LRC): First, we segment region of interest from mammogram images. Second, we calculate WEE from the segmented images. Third, LRC was used as the classifier. We named our method as “WEE + LRC”. The experiment used 10-fold stratified cross validation that was repeated 10 times. The statistical results showed the classification result was the best when the decomposition level was 4, with a sensitivity of 92.00 ± 3.20%, a specificity of 91.70 ± 3.27%, and an accuracy of 91.85 ± 2.21%. The proposed method was superior to other five state-of-the-art methods. In all, our method is effective in detecting abnormal breasts.

Keywords

Breast abnormality Digital mammography Wavelet energy entropy Linear regression classifier Least-squares estimation 

Notes

Acknowledgments

This paper was supported by NSFC (61602250, 61503188, 61562041, 61271374), Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982), Program of Natural Science Research of Jiangsu Higher Education Institutions (14KJB520021), Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology (2016WLZC013), Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology (30916014107).

References

  1. 1.
    (2016) The mini-MIAS database of mammograms. Available from: http://peipa.essex.ac.uk/info/mias.html
  2. 2.
    Abdel-Nasser M et al (2015) Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern. Exp Syst Appl 42(24):9499–9511CrossRefGoogle Scholar
  3. 3.
    Adamekova E et al (2003) The effect of psychoemotional stress on chemically induced mammary carcinogenesis in female rats. Biologia 58(5):991–994Google Scholar
  4. 4.
    Agarwal P (2016) Artificial intelligence and its applications 2014. Math Probl Eng, Article ID: 3871575Google Scholar
  5. 5.
    Ammari ML et al (2016) Feasible generalized least squares estimation of channel and noise covariance matrices for MIMO systems. Can J Electr Comput Eng 39(1):42–50CrossRefGoogle Scholar
  6. 6.
    Arnawa I (2015) Image enhancement using Homomorphic filtering and adaptive median filtering for Balinese Papyrus (Lontar). Int J Adv Comput Sci Appl 6(8):250–255Google Scholar
  7. 7.
    Balochian S (2014) Artificial intelligence and its applications. Math Probl Eng, Article ID: 840491Google Scholar
  8. 8.
    Cattani C, Rao R (2016) Tea category identification using a novel fractional Fourier Entropy and Jaya Algorithm. Entropy 18(3), Article ID: 77Google Scholar
  9. 9.
    Denis G, Strissel K (2015) Cardiometabolic abnormalities associate with an inflammatory cytokine profile in breast adipose tissue and plasma of obese African American women. J Immunol 194:2Google Scholar
  10. 10.
    Domingo L et al (2016) Cross-national comparison of screening mammography accuracy measures in US, Norway, and Spain. Eur Radiol 26(8):2520–2528CrossRefGoogle Scholar
  11. 11.
    Evangelista AL, Santos EMM (2012) Cluster of symptoms in women with breast cancer treated with curative intent. Supportive Care Cancer 20(7):1499–1506CrossRefGoogle Scholar
  12. 12.
    Gorgel P et al (2015) Computer-aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines. Expert Syst 32(1):155–164CrossRefGoogle Scholar
  13. 13.
    Gorriz JM, Ramírez J () Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Front Comput Neurosci 2016(10), Article ID: 160Google Scholar
  14. 14.
    Hemmati F et al (2016) Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Appl Acoust 104:101–118CrossRefGoogle Scholar
  15. 15.
    Ignatiadis M et al (2016) Liquid biopsy-based clinical research in early breast cancer: The EORTC 90091–10093 Treat CTC trial. Eur J Cancer 63:97–104CrossRefGoogle Scholar
  16. 16.
    Javed A et al (2016) Dynamic 3-D MR visualization and detection of upper airway obstruction during sleep using region-growing segmentation. IEEE Trans Biomed Eng 63(2):431–437CrossRefGoogle Scholar
  17. 17.
    Jeon S (2014) Haptically assisting breast tumor detection by augmenting abnormal lump. IEICE Trans Inf Syst E97D(2):361–365CrossRefGoogle Scholar
  18. 18.
    Kam JWY et al (2016) Sustained attention abnormalities in breast cancer survivors with cognitive deficits post chemotherapy: an electrophysiological study. Clin Neurophysiol 127(1):369–378MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kassayova M et al (2007) Effect of a short-term and long-term melatonin administration on mammary carcinogenesis in female Sprague–Dawley rats influenced by repeated psychoemotional stress. Acta Vet Brno 76(3):371–377CrossRefGoogle Scholar
  20. 20.
    Kolade VO, Meseeha MG (2016) Capsule commentary on tosteson et al., variation in screening abnormality rates and follow-Up of breast, cervical and colorectal cancer screening within the PROSPR consortium. J Gen Intern Med 31(4):411–411CrossRefGoogle Scholar
  21. 21.
    Leng XX et al (2016) A multi-scale plane-detection method based on the Hough transform and region growing. Photogramm Rec 31(154):166–192CrossRefGoogle Scholar
  22. 22.
    Li J (2016) Detection of left-sided and right-sided hearing loss via fractional Fourier transform. Entropy 18(5), Article ID: 194Google Scholar
  23. 23.
    Liu G (2016) Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform. Adv Mech Eng 8(2), Article ID: 11.Google Scholar
  24. 24.
    Liu Y, et al (2013) Extraction and analysis of EEG features under electric stimulation. In international conference on medical imaging physics and engineering (Icmipe). Shenyang, PEOPLES R CHINA. pp. 254–258Google Scholar
  25. 25.
    Liu G et al (2016) Detection of Alzheimer’s disease by three-dimensional displacement field estimation in structural magnetic resonance imaging. J Alzheimers Dis 50(1):233–248Google Scholar
  26. 26.
    Lu DY (2016) A hybrid optimization method for multiplicative noise and blur removal. J Comput Appl Math 302:224–233MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Majdak-Paredes EJ et al (2015) Integrated algorithm for reconstruction of complex forms of Poland syndrome: 20-year outcomes. J Plast Reconstr Aesthetic Surg 68(10):1386–1394CrossRefGoogle Scholar
  28. 28.
    Makandar A, Halalli B (2016) Threshold based segmentation technique for mass detection in mammography. J Comput 11(6):472–478Google Scholar
  29. 29.
    Martel-Billard C et al (2016) Trisomy 21 and breast cancer: a genetic abnormality which protects against breast cancer? Gynecol Obstet Fertil 44(4):211–217CrossRefGoogle Scholar
  30. 30.
    Matsuoka J et al (2016) Switching non-local vector median filter. Opt Rev 23(2):195–207CrossRefGoogle Scholar
  31. 31.
    Milosevic M et al (2015) Comparative analysis of breast cancer detection in mammograms and thermograms. Biomed Eng-Biomedizinische Technik 60(1):49–56Google Scholar
  32. 32.
    Mojra A et al (2009) Abnormal mass detection in a real breast model: a computational tactile sensing approach. In world congress on medical physics and biomedical engineering. Springer, Munich, GERMANY, pp 115–118Google Scholar
  33. 33.
    Munir A et al (2016) A review of 66 consecutive patients investigated for mammographic abnormalities by digital tomosynthesis guided vacuum assisted breast biopsy. Cancer Res 76:2CrossRefGoogle Scholar
  34. 34.
    Naseem I et al (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112CrossRefGoogle Scholar
  35. 35.
    Oztekin A et al (2016) A data analytic approach to forecasting daily stock returns in an emerging market. Eur J Oper Res 253(3):697–710MathSciNetCrossRefMATHGoogle Scholar
  36. 36.
    Phillips P (2016) Three-dimensional eigenbrain for the detection of subjects and brain regions related with Alzheimer’s disease. J Alzheimers Dis 50(4):1163–1179CrossRefGoogle Scholar
  37. 37.
    Phillips M, et al (2014) Rapid point-of-care breath test for biomarkers of breast cancer and abnormal mammograms. Plos One 9(3), Article ID: e90226Google Scholar
  38. 38.
    Phillips P et al (2015) Detection of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomed Signal Proc Control 21:58–73CrossRefGoogle Scholar
  39. 39.
    Racz JM et al (2016) Improving patient flow and timeliness in the diagnosis and management of breast abnormalities: the impact of a rapid diagnostic unit. Curr Oncol 23(3):E260–E265CrossRefGoogle Scholar
  40. 40.
    Renaudeau C et al (2016) Evaluation of sentinel lymph node biopsy after previous breast surgery for breast cancer: GATA study. Breast 28:54–59CrossRefGoogle Scholar
  41. 41.
    Seal A et al (2014) Histogram of bunched intensity values based thermal face recognition. In: Kryszkiewicz M et al (eds) Rough sets and intelligent systems paradigms. Springer-Verlag Berlin, Berlin, pp 367–374Google Scholar
  42. 42.
    Seigneurin A et al (2016) Overdiagnosis and overtreatment associated with breast cancer mammography screening: a simulation study with calibration to population-based data. Breast 28:60–66CrossRefGoogle Scholar
  43. 43.
    Tagliafico AS et al (2016) Diagnostic performance of contrast-enhanced spectral mammography: systematic review and meta-analysis. Breast 28:13–19CrossRefGoogle Scholar
  44. 44.
    Tahir MA et al (2011) Face recognition using multi-scale local phase quantisation and linear regression classifier. In international conference on image processing. IEEE, Brussels, BELGIUM, pp 765–768Google Scholar
  45. 45.
    Talib Z et al (2016) A community-oriented approach to breast cancer in a low-resource setting: improving awareness, early detection and treatment of breast cancer in Tajikistan. Breast J 22(3):330–334CrossRefGoogle Scholar
  46. 46.
    Wantanajittikul K et al (2016) Automatic cardiac T2*relaxation time estimation from magnetic resonance images using region growing method with automatically initialized seed points. Comput Methods Prog Biomed 130:76–86CrossRefGoogle Scholar
  47. 47.
    Wei G (2010) Color image enhancement based on HVS and PCNN. SCIENCE CHINA Inf Sci 53(10):1963–1976MathSciNetCrossRefGoogle Scholar
  48. 48.
    Winkel RR, et al (2016) Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case–control study. BMC Cancer 16 DOI:  10.1186/s12885-016-2450-7 (Online)
  49. 49.
    Wu X (2016) Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization. SIMULATION 92(9):873–885CrossRefGoogle Scholar
  50. 50.
    Xiao LM et al (2016) An enhancement method for X-ray image via fuzzy noise removal and homomorphic filtering. Neurocomputing 195:56–64CrossRefGoogle Scholar
  51. 51.
    Yang SN, et al (2015) Identification of breast cancer using integrated information from MRI and mammography. Plos One 10(6), Article ID: e0128404Google Scholar
  52. 52.
    Yu J, et al (2013) A new method for gyroscope fault diagnosis based on CGA RBFNN and multi-wavelet entropy. In international conference on Mechatronic sciences, electric engineering and computer. Shenyang, PEOPLES R CHINA. pp 39–43Google Scholar
  53. 53.
    Yu XY, et al (2016) Retrospective and comparative analysis of Tc-99 m-Sestamibi breast specific gamma imaging versus mammography, ultrasound, and magnetic resonance imaging for the detection of breast cancer in Chinese women. BMC Cancer 16 Article ID: 450Google Scholar
  54. 54.
    Yu WB et al (2016) Research of improved adaptive median filter algorithm. In international conference on electrical and information technologies for rail transportation: transportation. Zhuzhou, PEOPLES R CHINA, Springer, pp 27–34Google Scholar
  55. 55.
    Yuan TF (2015) Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci 9, Article ID: 66Google Scholar
  56. 56.
    Zaharescu E (2007) Morphological enhancement of medical images in a logarithmic image environment. In: Sanei S et al (eds) International conference on digital signal processing, 15th edn. Ieee, Cardiff, WALES, pp 171–174Google Scholar
  57. 57.
    Zhang Y, Wu L (2008) Improved Image Filter based on SPCNN. Sci China F: Inf Sci 51(12):2115–2125CrossRefGoogle Scholar
  58. 58.
    Zhou X-X (2016) Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation 92(9):861–871CrossRefGoogle Scholar
  59. 59.
    Zubor P et al (2015) Gene expression abnormalities in histologically normal breast epithelium from patients with luminal type of breast cancer. Mol Biol Rep 42(5):977–988CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Yi Chen
    • 1
    • 2
    • 3
  • Yin Zhang
    • 4
  • Hui-Min Lu
    • 5
  • Xian-Qing Chen
    • 6
    • 7
  • Jian-Wu Li
    • 8
  • Shui-Hua Wang
    • 1
    • 9
  1. 1.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  2. 2.Hunan Provincial Key Laboratory of Network Investigational TechnologyHunan Policy AcademyChangshaChina
  3. 3.Key Laboratory of Image and Video Understanding for Social SafetyNanjing University of Science and TechnologyNanjingChina
  4. 4.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina
  5. 5.Department of Mechanical and Control EngineeringKyushu Institute of TechnologyFukuoka PrefectureJapan
  6. 6.Department of electrical engineering, College of engineeringZhejiang Normal UniversityJinhuaChina
  7. 7.Department of Electrical EngineeringColumbia UniversityNew YorkUSA
  8. 8.School of Computer Science and Technology, Beijing Institute of TechnologyBeijingChina
  9. 9.Department of Electrical EngineeringThe City College of New York, CUNYNew YorkUSA

Personalised recommendations