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Wavelet energy entropy and linear regression classifier for detecting abnormal breasts

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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.

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References

  1. (2016) The mini-MIAS database of mammograms. Available from: http://peipa.essex.ac.uk/info/mias.html

  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):9499–9511

    Article  Google Scholar 

  3. Adamekova E et al (2003) The effect of psychoemotional stress on chemically induced mammary carcinogenesis in female rats. Biologia 58(5):991–994

    Google Scholar 

  4. Agarwal P (2016) Artificial intelligence and its applications 2014. Math Probl Eng, Article ID: 3871575

  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):42–50

    Article  Google Scholar 

  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):250–255

    Google Scholar 

  7. Balochian S (2014) Artificial intelligence and its applications. Math Probl Eng, Article ID: 840491

  8. Cattani C, Rao R (2016) Tea category identification using a novel fractional Fourier Entropy and Jaya Algorithm. Entropy 18(3), Article ID: 77

  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 194:2

    Google Scholar 

  10. Domingo L et al (2016) Cross-national comparison of screening mammography accuracy measures in US, Norway, and Spain. Eur Radiol 26(8):2520–2528

    Article  Google Scholar 

  11. Evangelista AL, Santos EMM (2012) Cluster of symptoms in women with breast cancer treated with curative intent. Supportive Care Cancer 20(7):1499–1506

    Article  Google Scholar 

  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):155–164

    Article  Google Scholar 

  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: 160

  14. Hemmati F et al (2016) Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Appl Acoust 104:101–118

    Article  Google Scholar 

  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 63:97–104

    Article  Google Scholar 

  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):431–437

    Article  Google Scholar 

  17. Jeon S (2014) Haptically assisting breast tumor detection by augmenting abnormal lump. IEICE Trans Inf Syst E97D(2):361–365

    Article  MathSciNet  Google Scholar 

  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):369–378

    Article  MathSciNet  Google Scholar 

  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):371–377

    Article  Google Scholar 

  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):411–411

    Article  Google Scholar 

  21. Leng XX et al (2016) A multi-scale plane-detection method based on the Hough transform and region growing. Photogramm Rec 31(154):166–192

    Article  Google Scholar 

  22. Li J (2016) Detection of left-sided and right-sided hearing loss via fractional Fourier transform. Entropy 18(5), Article ID: 194

  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.

  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. 254–258

  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):233–248

    MathSciNet  Google Scholar 

  26. Lu DY (2016) A hybrid optimization method for multiplicative noise and blur removal. J Comput Appl Math 302:224–233

    Article  MathSciNet  MATH  Google Scholar 

  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):1386–1394

    Article  Google Scholar 

  28. Makandar A, Halalli B (2016) Threshold based segmentation technique for mass detection in mammography. J Comput 11(6):472–478

    Article  Google Scholar 

  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):211–217

    Article  Google Scholar 

  30. Matsuoka J et al (2016) Switching non-local vector median filter. Opt Rev 23(2):195–207

    Article  Google Scholar 

  31. Milosevic M et al (2015) Comparative analysis of breast cancer detection in mammograms and thermograms. Biomed Eng-Biomedizinische Technik 60(1):49–56

    Google Scholar 

  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 115–118

    Google Scholar 

  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 76:2

    Google Scholar 

  34. Naseem I et al (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112

    Article  Google Scholar 

  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):697–710

    Article  MathSciNet  MATH  Google Scholar 

  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):1163–1179

    Article  Google Scholar 

  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: e90226

  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 21:58–73

    Article  Google Scholar 

  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–E265

    Article  Google Scholar 

  40. Renaudeau C et al (2016) Evaluation of sentinel lymph node biopsy after previous breast surgery for breast cancer: GATA study. Breast 28:54–59

    Article  Google Scholar 

  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 367–374

    Chapter  Google Scholar 

  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 28:60–66

    Article  Google Scholar 

  43. Tagliafico AS et al (2016) Diagnostic performance of contrast-enhanced spectral mammography: systematic review and meta-analysis. Breast 28:13–19

    Article  Google Scholar 

  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 765–768

    Google Scholar 

  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):330–334

    Article  Google Scholar 

  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 130:76–86

    Article  Google Scholar 

  47. Wei G (2010) Color image enhancement based on HVS and PCNN. SCIENCE CHINA Inf Sci 53(10):1963–1976

    Article  MathSciNet  Google Scholar 

  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. 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):873–885

    Article  Google Scholar 

  50. Xiao LM et al (2016) An enhancement method for X-ray image via fuzzy noise removal and homomorphic filtering. Neurocomputing 195:56–64

    Article  Google Scholar 

  51. Yang SN, et al (2015) Identification of breast cancer using integrated information from MRI and mammography. Plos One 10(6), Article ID: e0128404

    Article  Google Scholar 

  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 39–43

  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: 450

  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 27–34

    Google Scholar 

  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: 66

  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 171–174

    Google Scholar 

  57. Zhang Y, Wu L (2008) Improved Image Filter based on SPCNN. Sci China F: Inf Sci 51(12):2115–2125

    Google Scholar 

  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):861–871

    Article  Google Scholar 

  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):977–988

    Article  Google Scholar 

Download references

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).

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Correspondence to Shui-Hua Wang.

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Chen, Y., Zhang, Y., Lu, HM. et al. Wavelet energy entropy and linear regression classifier for detecting abnormal breasts. Multimed Tools Appl 77, 3813–3832 (2018). https://doi.org/10.1007/s11042-016-4161-0

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