Characterization of mammographic masses based on local photometric attributes


This paper proposes Local Photometric Attributes (LPA) for the characterization of mammographic masses as benign or malignant. LPA measures the local information over the optical density image which suppresses the background region and provides more details about the mass lesion. The evaluation of the proposed approach is conducted by incorporating the mammograms of two benchmark databases—mini-MIAS and DDSM where a ten-fold cross validation technique is employed with different classifiers—Fishers Linear Discriminant Analysis, Random forest, and Support vector machine after filtering the optimal set of features by utilizing stepwise logistic regression method. The best performance achieved by the introduced approach in terms of an area under the receiver operating characteristic (ROC) curve (Az value) and accuracy (Acc) are 0.94 and 86.90%, respectively for the mini-MIAS dataset while the same for the DDSM dataset are 0.89 and 80.76%, respectively. The competitive nature of the proposed scheme is evident by comparing the obtained results with schemes in the state-of-the-arts.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.

    (ACS) A.C.S Global cancer facts and figures. (accessed on October 2016) (3rd Edition 2016)

  2. 2.

    Bowyer K, Moore R, Heath MD, Kegelmeyer WP (2001) Digital database for screening mammography. In: Proceedings of the Fifth International Workshop on Digital Mammography. Medical Physics Publishing

  3. 3.

    Breiman L (2005) Random forests. Mach Learn 45(1):5–32

    MATH  Article  Google Scholar 

  4. 4.

    Bojar K, Nieniewski M (2008) New features for classification of cancerous masses in mammograms based on morphological dilation. In: 5th International Conference on Visual Information Engineering, (VIE), pp 111–116

  5. 5.

    Buciu I, Gacsadi A (2011) Directional features for automatic tumor classification of mammogram images. Biomed Signal Process Control 6:370–378

  6. 6.

    Calas MJG, Gutfilen B, de Albuquerque Pereira WC (2012) CAD and mamography: why use this tool? Radiol Brasil 45:46–52

  7. 7.

    Chakraborty J, Midya A, Mukhopadhyay S, Sadhu A (2013) Automatic characterization of masses in mammograms. In: 6th International Conference on Biomedical Engineering and Informatics (BMEI), pp 111–115

  8. 8.

    Chakraborty J, Mukhopadhyay S, Singla V, Khandelwal N, Rangayyan R (2012) Detection of masses in mammograms using region growing controlled by multilevel thresholding. In: 25th International Symposium on Computer-Based Medical Systems (CBMS), pp 1–6

  9. 9.

    Duda RO, Stork DG, Hart PE (2001) Pattern classification, 2nd edn. Wiley-Interscience, New York

    MATH  Google Scholar 

  10. 10.

    Eltoukhy M, Elhoseny M, Hosny M, Singh A (2018) Computer aided detection of mammographic mass using exact gaussian–hermite moments. Journal of Ambient Intelligence and Humanized Computing

  11. 11.

    Gorgel P, Sertbas A, Ucan ON (2013) Mammographical mass detection and classification using local seed region growing-spherical wavelet transform (LSRG-SWT) hybrid scheme. Comput Biol Med 43:765–774

  12. 12.

    Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621

    Article  Google Scholar 

  13. 13.

    Homer MJ Mammographic Interpretation: A Practical Approach, 2nd edn. McGraw-Hill, NY

  14. 14.

    John A, Gonzáleza FA, Ramos-Pollánb R, Oliveirac JL, Lopez MAG (2016) Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Prog Biomed 21(127):248–257

    Google Scholar 

  15. 15.

    Laroussi M, Ben Ayed N, Masmoudi A, Masmoudi D (2013) Diagnosis of masses in mammographic images based on Zernike moments and Local binary attributes. In: World Congress on Computer and Information Technology (WCCIT), pp 1–6

  16. 16.

    Liu X, Liu J, Zhou D, Tang J (2010) A benign and malignant mass classification algorithm based on an improved level set segmentation and texture feature analysis. In: 4th International Conference on Bioinformatics and Biomedical Engineering (ICBBE), pp 1–4

  17. 17.

    Liu X, Xu X, Liu J, Feng Z (2011) A new automatic method for mass detection in mammography with false positives reduction by supported vector machine. In: 4th International Conference on Biomedical Engineering and Informatics (BMEI), pp 33–37

  18. 18.

    Midya A, Chakraborty J (2015) Classification of benign and malignant masses in mammograms using multi-resolution analysis of oriented patterns. In: 12th International Symposium on Biomedical Imaging (ISBI), pp 411–414

  19. 19.

    Midya A, Rabidas R, Sadhu A, Chakraborty J (2018) Edge weighted local texture features for the categorization of mammographic masses. J Med Biol Eng 38 (3):457–468

    Article  Google Scholar 

  20. 20.

    Mudigonda NR, Rangayyan R, Desautels JEL (2000) Gradient and texture analysis for the classification of mammographic masses. IEEE Trans Med Imaging 19 (10):1032–1043

    Article  Google Scholar 

  21. 21.

    Muramatsu C, Hara T, Endo T, Fujita H (2016) Breast mass classification on mammograms using radial local ternary patterns. Comput Biol Med 72:43–53

  22. 22.

    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987

    MATH  Article  Google Scholar 

  23. 23.

    Pramanik S, Ghosh S, Bhattacharjee D, Nasipuri M (2019) Segmentation of breast-region in breast thermogram using arc-approximation and triangular-space search (BATS). IEEE Transactions on Instrumentation and Measurement:1–1

  24. 24.

    Pramanik S, Bhattacharjee D, Nasipuri M (2019) MSPSF: a multi-scale local intensity measurement function for segmentation of breast thermogram. IEEE Transactions on Instrumentation and Measurement:1-1

  25. 25.

    Pramanik S, Banik D, Bhattacharjee D, Nasipuri M, Bhowmik M.K, Majumdar G (2019) Suspicious-region segmentation from breast thermogram using DLPE-based level set method. IEEE Trans Med Imaging 38(2):572–584

    Article  Google Scholar 

  26. 26.

    Pomponiu V, Hariharan H, Zheng B, Gur D (2014) Improving breast mass detection using histogram of oriented gradients. In: SPIE Medical Imaging- 2014: Computer Aidded Diagnosis, Vol 9035, pp 90351R–90351R

  27. 27.

    Qiu Y, Yan S, Tan M, Cheng S, Liu H, Zheng B (2016) Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study. In: SPIE Medical Imaging- 2016: Computer Aidded Diagnosis. Vol 9785, pp 978520–978520–6

  28. 28.

    Rabidas R, Chakraborty J, Midya A (2016) Analysis of 2D singularities for mammographic mass classification. IET Comput Vis 11(1):22–32

    Article  Google Scholar 

  29. 29.

    Rabidas R, Midya A, Sadhu A, Chakraborty J (2016) Benign-malignant mass classification in mammogram using edge weighted local texture features. In: Proceedings of SPIE Medical Imaging, Vol 9785, pp 97851X–97851X–6

  30. 30.

    Rabidas R, Midya A, Chakraborty J (2018) Neighborhood structural similarity mapping for the classification of masses in mammograms. IEEE J Biomed Health Inf 22:826–834

    Article  Google Scholar 

  31. 31.

    Rabidas R, Midya A, Chakraborty J, Sadhu A, Arif W (2018) Multi-resolution analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification. In: Proceedings of SPIE Med. Imaging. 105752N

  32. 32.

    Ramsey FL, Schafer DW (1997) The statistical sleuth: a course in methods of data analysis. Duxbury Press, CA

  33. 33.

    Sahiner B, Chan HP, Petrick N, Helvie MA, Goodsitt MM (1998) Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis. Med Phys 24:516–526

    Article  Google Scholar 

  34. 34.

    Sahiner B, Chan HP, Petrick N, Helvie MA, Hadjiiski LM (2001) Improvement of mammographic mass characterization using spiculation measures and morphological features. Med Phys 28(7):1455–1465

    Article  Google Scholar 

  35. 35.

    Sameti M, Ward R, Morgan-Parkes J, Palcic B (2009) Image feature extraction in the last screening mammograms prior to detection of breast cancer. IEEE J Sel Top Signal Process 3:46–52

    Article  Google Scholar 

  36. 36.

    Serifovic-Trbalic A, Trbalic A, Demirovic D, Prljaca N, Cattin P (2014) Classification of benign and malignant masses in breast mammograms. In: 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp 228–233

  37. 37.

    Suckling J (1994) The mammographic image analysis society digital mammogram database exerpta medica. Int Congr Ser 1069:375–378

    Google Scholar 

  38. 38.

    Suhail Z, Hamidinekoo A, Zwiggelaar R (2018) Mammographic mass classification using filter response patches. IET Comput Vis 12:1060–1066

    Article  Google Scholar 

  39. 39.

    Sun L, Li L, Xu W, Liu W, Zhang J, Shao G (2010) A novel classification scheme for breast masses based on multi-view information fusion. In: 4th International Conference on Bioinformatics and Biomedical Engineering (ICBBE), pp 1–4

  40. 40.

    Tahmasbi A, Saki F, Shokouhi SB (2011) Classification of benign and malignant masses based on zernike moments. Comput Biol Med 41:726–735

  41. 41.

    Tai SC, Chen ZS, Tsai WT (2014) An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Inf 18:618–627

    Article  Google Scholar 

  42. 42.

    Tan M, Pu J, Zheng B (2014) Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model. Int J CARS 9:1–16

    Article  Google Scholar 

  43. 43.

    Vadivel A, Surendiran B (2013) A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories. Comput Biol Med 43:259–267

  44. 44.

    Vapnik V (2000) The nature of statistical learning theory, Statistics for Engineering and Information Science. Springer, Berlin

  45. 45.

    Wei CH, Chen SY, Liu X (2012) Mammogram retrieval on similar mass lesions. Comput Methods Program Biomed 106:234–248

  46. 46.

    Zhang X, Sasaki T, Suzuki S, Takane Y, Kawasumi Y, Ishibashiz T, Homma N, Yoshizawa M (2017) Classification of mammographic masses by deep learning. In: Proceedings of Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp 793–396

Download references

Author information



Corresponding author

Correspondence to Rinku Rabidas.

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

Verify currency and authenticity via CrossMark

Cite this article

Rabidas, R., Arif, W. Characterization of mammographic masses based on local photometric attributes. Multimed Tools Appl 79, 21967–21985 (2020).

Download citation


  • Breast cancer
  • Mammography
  • Mass classification
  • ODCM
  • Local attributes