Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3941–3962 | Cite as

Mammogram classification using dynamic time warping

  • Syed Jamal Safdar Gardezi
  • Ibrahima Faye
  • Jose M. Sanchez Bornot
  • Nidal Kamel
  • Mohammad Hussain


This paper presents a new approach for breast cancer classification using time series analysis. In particular, the region of interest (ROI) in mammogram images is classified as normal or abnormal using dynamic time warping (DTW) as a similarity measure. According to the analogous case in time series analysis, the DTW subsumes Euclidean distance (ED) as a specific case with increased robustness due to DTW flexibility to address local horizontal/vertical deformations. This method is especially attractive for biomedical image analysis and is applied to mammogram classification for the first time in this paper. The current study concludes that varying the size of the ROI images and the restriction on the search criteria for the warping path do not affect the performance because the method produces good classification results with reduced computational complexity. The method is tested on the IRMA and MIAS dataset using the k-nearest neighbour classifier for different k values, which produces an area under curve (AUC) value of 0.9713 for one of the best scenarios.


Dynamic time warping Mammogram classification Orientation False alarms Type II error Sensitivity 



This research was supported by the URIF grant 0153AA-B52.

Author’s contributions

SJS, IF and JMSB proposed the idea; participated in implementation and coordinated in optimization of study parameters using matlab. SJSG and JMSB also performed the literature survey and worked on database construction. NK and MH provided valuable suggestions in design and implementation of study and assisted in drafting of the manuscript. All authors have read and approved the final manuscript.

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Syed Jamal Safdar Gardezi
    • 1
    • 2
  • Ibrahima Faye
    • 1
    • 2
  • Jose M. Sanchez Bornot
    • 3
  • Nidal Kamel
    • 1
    • 4
  • Mohammad Hussain
    • 5
  1. 1.Centre for Intelligent Signal and Imaging Research (CISIR)Universiti Teknologi PetronasSeri IskanadarMalaysia
  2. 2.Department of Fundamental and Applied SciencesUniversiti Teknologi PETRONASSeri IskanadarMalaysia
  3. 3.University of UlsterLondonderryUK
  4. 4.Department of Electrical and Electronics EngineeringUniversiti Teknologi PETRONASSeri IskanadarMalaysia
  5. 5.Department of Computer Science, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia

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