Microcalcifications detection in mammographic images using texture coding

Applied Mathematical Sciences

Volumn 5, Issue 5-8, 2011, Pages 381-393

  Eddaoudi, Fatima ^a   Regragui, Fakhita ^a  

^a FACULTÉ DES SCIENCES   (Morocco)

Author keywords

Mammography; Microcalcification; Rank coding; SVM; Texture coding

Indexed keywords

EID: 79952123244     PISSN: None     EISSN: 1312885X     Source Type: Journal    
DOI: None     Document Type: Article

References (27)

1. A.A.Ferreira, J.F.Nascimento, R.T.Ing, Analysis of mammogram using self-organizing neural networks based on spatial isomorphism, Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, August,2007, 12:17.
2. Baraldi, F. Parmiggiani. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing, march 1995, 33(2), 293:304.
3. C. Bastos R. Ferreira, D.L. Borges, Analysis of mammogram classification using a wavelet transform decomposition, Pattern Recognition Letters, 2003, V.24, Issue 7, 973:982.
4. C.C.Gotlieb, H.E.Kreyszig, Texture desciptors based on co-occurence matrices. Computer Vision, Graphics and Image Processing, 1990, Vol. 51, Issue 1, 70:86.
5. D.Bi, Segmentation d'images basée sur les statistiques de rangs des niveaux de gris. PhD thesis, Université de Tours, janvier 1997.
6. D.Sumeet, S.Harpreet, H.W.Thompson, Associative classification of mammograms using weighted rules, Expert Systems with Applications, ELSEVIER, 2009, 36, 9250:9259.
7. E.E.O.Júlia, T.M. Deserno, A.Araújo. Breast Lesions Classification applied to a reference database, 2nd International Conference: E-Medical Systems, 2008, October 29-31, Tunisia.
8. F. Eddaoudi F. Regragui, K. Laraki, Characterization of normal mammograms based on the Statistical Features, Proc. Second International Symposium on Communications, Control and Signal Processing (ISCCSP), 2006, Marrakech.
9. F.Ouslimani, Z.Ameur, S.Ameur, Segmentation d'images texturées basée sur le codage d'extrémalité locale, 3èmes Journées Internationales sur l'Informatique Graphique, JIG'2007.
10. G.E.Lowitz, Can a local histogram really map texture information, Pattern Recognition, 1983, vol. 16, n° 2, 141:147.
11. G.Lohmann. Analysis and Synthesis of Textures: a Co-occurrence-Based Approach. Computer and Graphics, 1995, Vol. 19, No. 1, 29:36.
12. J.Parkkinen, T.J. Selkainaho, Detecting texture periodicity from the cooccurrence matrix. Pattern Recognition Letters, 1990, Vol. 11, 43:50.
13. K. Bentley et al, Mammography image quality: Analysis of evaluation criteria using pectoral muscle presentation, Radiography (2007), doi:10.1016/j.radi.2007.02.002.
14. L.Heucke, M.Knaak, R.Orglmeister, A new image segmentation method based on human brightness perception and foveal adaptation. IEEE Signal Processing Letters, 2000, 7(6),129: 131.
15. M.Hanifi, F. Sedes, D. Aboutajdine, A. Lasfar. Extraction de caractéristiques de texture d'images satellites pour la classification. 2èmes Rencontres Inter associations (RIAs2006) sur la classification et ses applications, Laboratoire ERIC, Université Lumière Lyon2, 20-21 Mars 2006, 50:51.
16. M.Hanifi, F.Sedes, D.Aboutajdine, A.Lasfar. A new approach for coding Satellite. Images: Rank coding. International Journal of Computational Science. 2009, Vol. 3, No. 1.
17. M.X.Ribeiro, A.J.M.Traina, A.G.R.Balan, C.Jr.Traina, P.M.A.Marques, In SuGAR, A framework to support mammogram diagnosis. IEEE CBMS Slovenia: Maribor, 2007, 47:52.
18. N.Karssemeijer, Adaptive noise equalisation and recognition of microcalcification clusters in mammograms. International Journal of Pattern Recognition and Artificial Intelligence 7 (6), 1993, 1357:1372.
19. O.Whi-Vin, K.KwangGi, K.Young-Jae, K.HanSung, R.JungSil, M.WooKyung, Detection of Microcalcifications in Digital Mammograms Using Foveal Method, Journal of Korean Society of Medical Informatics,2009, 15-1, 165:172.
20. P.Gorgel, A.Sertbas, N.Kilic, O.N.Ucan, O.Osman, Mammographic mass classification using wavelet based support vector machine, Journal of Electrical & Electronics Engineering, 2009, V9, N°1, 867:875.
21. R.M. Haralick, Statistical and structural approaches to texture, Proceeding of IEEE, Vol.67, N°5,1979, p.786-804.
22. S.Don, C.Duckwon, K.Revathy, C.Keunmi, M.Dugki, A neural network approach to mammogram image classification using fractal features, Intelligent Computing and Intelligent Systems, 2009, V.4, 444:447.
23. S.S.Jasjit, M.R.Rangaraj, "Recent Advances In Breast Imaging, Mammography and Computer-Aided Diagnosis of Breast Cancer", Spie Press, Belligham, Washington USA, (2006) ISBN: 9780819460813.
24. W.Liyang, Y.Yongyi, M.N.Robert, Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis, Pattern Recognition 42, 2009, 1126:1132.
25. W.Zhang, et al., Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms. Med Phys, 1998. 25(6), 949:56.
26. X.Weidong, X.Shunren, D.Huilong, A Novel Computer-Aided Diagnosis System of the Mammograms, Intelligent Computing in Signal Processing and Pattern Recognition, 2006, 345, 639:644.
27. Z.Ameur, Codage des images en vue d'une segmentation de haut niveau: Application aux images satellitaires, thèse de doctorat en électronique, option télédétection, UMMTO, Algérie, mai 2007.