The week's pick
Random Articles
Reseach Article
Robust Textural Approach for False Positive Reduction in Mammographic Mass
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 68 - Number 6 |
| Year of Publication: 2013 |
| Authors: Norhene Gargouri Ben Ayed, Alima Damak Masmoudi, Dorra Sellami Masmoudi |
10.5120/11580-6902
|
Norhene Gargouri Ben Ayed, Alima Damak Masmoudi, Dorra Sellami Masmoudi . Robust Textural Approach for False Positive Reduction in Mammographic Mass. International Journal of Computer Applications. 68, 6 ( April 2013), 1-5. DOI=10.5120/11580-6902
Abstract
In the last few years and over the last decade, different algorithms have been introduced for mass detection in digital mammograms. However, approximately all the proposed methods present a high number corresponding to false positif. In this paper, we propose a novel approach dealing with the problem of false positif reduction. This proposal is based on the use of a textural approach for the description of the masses texture properties. The least square estimation is applied to minimize the local difference in order to obtain more stable directional features, this scheme is called adaptative LBP (ALBP) to improve the features extracted using LBP efficiently. Artificial Neural Network (ANN) are used for the classification of the breast tissue. The evaluation of this approach is based on 1792 Regions Of Interest (ROIs) obtained from the Digital Database Of Screening Mammography (DDSM). The obtained results show that the proposed textural approach is effective in feature extraction and allows mass false positive reduction. Moreover, this proposal demonstrates better performance where compared to the methods of the state of the art.
References
- Eurostat, Health statistics Atlas on mortality in the European Union, Of?ce for Of?cial Publications of the European Union; 2002.
- American Cancer Society Breast cancer: facts and ?gures. ACS; 2003–2004.
- Esteve J, Kricker A, Ferlay J, Parkin D. Facts and ?gures of cancer in the European Community. In: Tech. rep. , International Agency for Research on Cancer. 1993. for young women with breast carcinoma. Cancer 2003;97(2):352–8.
- Sickles EA. Breast cancer screening outcomes in women ages 40–49: clinical experience with service screening using modern mammography. J Natl Cancer Inst: Monogr 1997;22:99–104.
- De Koning HJ, Fracheboud J, Boer R, Verbeek AL, Collette HJ, Hendriks JHCL. Nation-wide breast cancer screening in the Netherlands: support for breast cancer mortality reduction. National evaluation team for breast cancer screening, Int J Cancer 1995;60(6):777–80.
- Bird RE, Wallace TW, Yankaskas BC. Analysis of cancers missed at screening mammography. Radiology 1992;184:613–7.
- Basset LW, Gold RH. Breast Cancer Detection: Mammograms and Other Methods in Breast Imaging. New York: Grune & Stratton; 1987.
- Sahiner B, Chan HP, Wei D, Petrick N, Helvie MA, Adler DD. Image feature selection by a genetic algorithm: Application to classi?cation of mass and normal breast tissue. Med Phys 1996;23:1671–84.
- Varela C, Tahoces PG, Méndez AJ, Souto M, Vidal JJ. Computerized detection of breast masses in digitized mammograms. Comput Biol Med 2007;37(2):214–26.
- Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classi?cation with local binary patterns. IEEE Trans Pattern Anal Machine Intell 2002;24(7):971–87.
- M. A. Turk & A. P. Pentland. "Eigenfaces for recognition. " J. Cognitive Neurosci. 3(1), pp. 71–86, 1991.
- Oliver A, Martí J, Martí R, Bosch A, Freixenet J. A new approach to the classi?cation of mammographic masses and normal breast tissue. IAPR Int Conf Pattern Recognit 2006;4:707–10.
- Lladó, X. , Oliver, A. , Freixenet, J. , Martí, R. , Martí J. "A textural approach for mass false positive reduction in mammography," Computerized Medical Imaging and Graphics 33, 415–422, 2009.
- N. Gargouri Benayed, A. Damak Masmoudi, D. Sellami Masmoudi and R. Abid : A new GLLD operator for mass detection in digital mammograms, International Journal of Biomedical Imaging.
- D. E. Rumelhart and G. E. Hinton, in Learning Internal Representations By Error Propagation, Parallel Distributed Processing, vol. 1, pp. 318–362, MIT Press, Cambridge, Mass, USA, 1986.
- Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer PJ. The digital database for screening mammography. Int Work Dig Mammogr 2000:212–8.
- Oliver, A. , Llad´o, X. , Mart´i, J. , Mart´i, R. , Freixenet, J. : False positive reduction in breast mass detection using two-dimensional PCA. In: Lect. Not. in Comp. Sc. , vol. 4478, pp. 154–161 (2007)
- Sahiner B, Chan HP, Wei D, Petrick N, Helvie MA, Adler DD. Image feature selection by a genetic algorithm: Application to classi?cation of mass and normal breast tissue. Med Phys 1996;23:1671–84.
Index Terms
Keywords