Empirical Descriptors Evaluation for Mass Malignity Recognition

Imene Cheikhrouhou, Khalifa Djemal, Dorra Sellami Masmoudi, Hichem Maaref, Nabil Derbel



In breast cancer field, radiologists and researchers aim to discriminate between masses due to benign breast diseases and tumors due to breast cancer. In general, benign masses have circumscribed contours, whereas, malignant tumors appear with spiculated and irregular boundaries. Recently, we proposed an original mass description based on three morphological mass descriptors, which are SPICULation (SPICUL), Contour Derivative Variation (CDV) and Skeleton End Points (SEP). In this paper, we detail an empirical mass evaluation based on these morphological descriptors which intend to distinguish between malignant and benign lesions. This evaluation is, first, assured by following descriptors evolution in two independent data sets: Alberta and MIAS. Secondly, for these two data sets, the Receiver Operating Characteristics (ROC) analysis is applied. A comparison between the classic use of Area (A) and Perimeter (P) descriptors only, and a combination with our three original evaluated descriptors is done. Obtained results proves that classification accuracy of the descriptors combination including: SPICUL, SEP, CDV, A and P outperforms that of the classic descriptors: A and P. Indeed, our original mass description provides the best Area under ROC Az = 0.986 for Alberta data set and Az = 0.9792 for the MIAS data set. Therefore, we affirm that our three original descriptors can serve as good shape descriptors for the benign-versus-malignant classification of breast masses on mammograms.


  1. C-M Chen, Y-H Chou, K-C Han, G-S Hung, C-M Tiu, H-J Chiou, S-Y Chiou, ”Breast Lesions on Sonograms: Computer-aided Diagnosis with Nearly Setting-Independent Features and Artificial Neural Networks”, Radiology, Fvrier 2003, p504-514.
  2. H-K Chiang, C-M Tiu, G-S Hung, S-C Wu, T-Y Chang, Y-H Chou, ”Stepwise Logistic Regression Analysis of Tumor Contour Features for Breast Ultrasound Diagnosis”, IEEE Ultrasonic Symposium, 2001, p1303-1306
  3. D-R Chena, R-F Changb, C-J Chenb, M-F Hob, S-J Kuoa, S-T Chena, S-J Hungc, W-K Moond, ”Classification of breast ultrasound images using fractal feature”, Elseiver, Journal of Clinical Imaging Vol, 29, 2005, p 235-245
  4. S Kim and S Yoon, ”Bi-rads features-based computer-aided diagnosis of abnormalities in mammographic images”. In 6th International Special Topic Conference on ITAB,(2007).
  5. A Oliver, J Freixenet, R Marti, J Pont, E Prez, E-R-E. Denton and R Zwiggelaar, ”A Novel Breast Tissue Density Classification Methodology”, IEEE Transactions On Information Technology In Biomedicine, Vol. 12, No. 1, January 2008, p55-65.
  6. American College of Radiology ”BI-RADS (Breast Imaging Reporting and Data System”) Frensh Edition realized by SFR (Societe Francaise de Radiologie), Third Edition, 2003.
  7. Alberta Program for the Early Detection of Breast Cancer. Alberta Cancer Board, 2001.
  8. H Rangayyan, R., and J Desautels, ”Content-based retrieval and analysis of mammographic masses”. In Journal of Electronic Imaging.(2005).
  9. The mammographic image analysis society digital mammogram database. In http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html.
  10. S Lam, Y., and Y Hong, ”Blood vessel extraction based on mumford shah model and skeletonization”. In Proceedings of the Fifth International Conference on Machine Learning and Cybernetics.(2006) Press.
  11. B Jahne, ”Digital image processing : concepts, algorithms, and scientific applications” (1993). In Springer-Verlag.
  12. L Wei, Y Yang, R M. Nishikawa, and Yu Jiang ”A Study on Several Machine-Learning Methods for Classification of Malignant and Benign Clustered Microcalcifications”, IEEE Transactions On Medical Imaging, Vol. 24, NO. 3, March 2005, p371-380
  13. K Djemal, W Puech and B Rossetto. Active Contours Propagation in a Medical Images Sequence With a Local Estimation, European Signal Processing Conference. EUSIPCO'02, Volume 1, pp: 41-44, September 2002, Toulouse, France.
  14. I Cheikhrouhou, K Djemal, D Sellami, N Derbel and H Maaref ”New mass description in mammographies”. 1st International Workshops on Image Processing Theory, Tools & Applications, IPTA'08, Sousse, Tunisia, 24-26 November 2008.
  15. I Cheikhrouhou, K Djemal, D Sellami Masmoudi, N Derbel and H Maaref. Abnormalities description for breast cancer recognition. Intenational Conference on E-Medical Systems, Morroco, 2007, p198-205.
  16. K Djemal, W Puech and B Rossetto. Automatic Active Contours Propagation in a Sequence of Medical Images. International Journal of Images and Graphics. vol.6 , n. 2, pp :267-292 , 2006.
  17. NR Mudigonda, RM Rangayyan, JEL Desautels: ”Gradient and texture analysis for the classification of mammographic masses”. IEEE Trans Med Imag 2000, p1032-1043.
  18. K Djemal, F Bouchara and B Rossetto. Image Modeling and Regionbased Active Contours Segmentation. Int. Conf. Vision, Modeling and Visualization VMV'02, ISBN:3-89838-034- 3, pp: 363-370, Novembre 2002, Erlangen, Germany.
  19. R-M. Rangayyan and T-M. Nguyen ”Fractal Analysis of Contours of Breast Masses in Mammograms”, Journal of Digital Imaging, Vol 20, No 3, 2007, p223-237.
  20. Q Guo, V Ruiz, J Shao, F Guo ”A novel approach to mass abnormality detection in mammographic images”, In Proceedings of the IASTED International Conference on Biomedical Engineering, Innsbruck, Austria, 2005, p180-185.
  21. K Djemal. Speckle reduction in ultrasound images by minimization of total Variation. IEEE International Conference on Image Processing (ICIP), Volume 3, ISBN: 0-7803-9134-9, pp: 357-360, September 2005, Genova, Italya.
  22. K Djemal, W Puech and B Rossetto. Geometric Active Contour Model Using Level Set Methods For Objects Tracking In Images Sequence. International Conference on Sciences of Electronics, Technology of Information and Telecommunication, SETIT'04, Mars 2004, Sousse, Tunisia.

Paper Citation

in Harvard Style

Cheikhrouhou I., Djemal K., Sellami Masmoudi D., Maaref H. and Derbel N. (2009). Empirical Descriptors Evaluation for Mass Malignity Recognition . In Proceedings of the 1st International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: Workshop MIAD, (BIOSTEC 2009) ISBN 978-989-8111-77-7, pages 91-100. DOI: 10.5220/0001815400910100

in Bibtex Style

@conference{workshop miad09,
author={Imene Cheikhrouhou and Khalifa Djemal and Dorra Sellami Masmoudi and Hichem Maaref and Nabil Derbel},
title={Empirical Descriptors Evaluation for Mass Malignity Recognition},
booktitle={Proceedings of the 1st International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: Workshop MIAD, (BIOSTEC 2009)},

in EndNote Style

JO - Proceedings of the 1st International Workshop on Medical Image Analysis and Description for Diagnosis Systems - Volume 1: Workshop MIAD, (BIOSTEC 2009)
TI - Empirical Descriptors Evaluation for Mass Malignity Recognition
SN - 978-989-8111-77-7
AU - Cheikhrouhou I.
AU - Djemal K.
AU - Sellami Masmoudi D.
AU - Maaref H.
AU - Derbel N.
PY - 2009
SP - 91
EP - 100
DO - 10.5220/0001815400910100